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It brings together Cortex Agents, NVIDIA BioNeMo NIMs, Snowpark Python tools, and a Snowflake App Runtime (SAR) web application into a single governed environment where computational biologists, medicinal chemists, structural biologists, and clinical data scientists can run AI-driven discovery workflows.\u003C/p\u003E\n","\u003Cp\u003EBy the end of this guide you will have a fully deployed Scientific Workbench with 28 agent-callable tools, a multi-agent Discovery Agent, 4 Cortex Analyst semantic views, reference datasets across 5 scientific domains, and a React web application — all running in your Snowflake account.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/scientific-workbench-for-life-sciences/architecture_diagram.png?v=cef43ec8\" alt=\"Architecture Diagram\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account (Enterprise edition or higher recommended)\u003C/li\u003E\u003Cli\u003EACCOUNTADMIN access (for initial setup; can be reduced after deployment)\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/index\"\u003ESnowflake CLI\u003C/a\u003E (\u003Ccode\u003Esnow\u003C/code\u003E) installed\u003C/li\u003E\u003Cli\u003ENVIDIA API key from \u003Ca href=\"https://build.nvidia.com\"\u003Ebuild.nvidia.com\u003C/a\u003E (free tier available)\u003C/li\u003E\u003Cli\u003ENode.js 20+ and npm (for local development only)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to deploy a multi-agent AI platform on Snowflake\u003C/li\u003E\u003Cli\u003EHow to configure NVIDIA BioNeMo NIM tools for drug discovery\u003C/li\u003E\u003Cli\u003EHow to set up role-based access control for scientific teams\u003C/li\u003E\u003Cli\u003EHow to build and deploy a Snowflake App Runtime (SAR) web application\u003C/li\u003E\u003Cli\u003EHow to run end-to-end drug discovery workflows through a conversational agent\u003C/li\u003E\u003Cli\u003EHow to extend the platform with new tools and data sources\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EResource\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDetails\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStorage (reference data)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~40 GB\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStorage (platform tables)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E< 1 GB\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarehouses\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3 (XS, S, ML) with auto-suspend\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Search\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2 services (catalog + tools)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMarketplace subscriptions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPubMed CKE, ClinicalTrials.gov CKE\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGPU compute\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENone required (NVIDIA hosted API)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMonthly credits (estimate)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E500 - 1,000\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E28 agent-callable tools: 7 native Snowpark Python, 11 NVIDIA BioNeMo NIM wrappers, 3 search tools, 2 MONAI imaging tools, 3 Parabricks genomics tools, 2 multi-NIM pipelines\u003C/li\u003E\u003Cli\u003E7 Cortex Agents: Genomics, Chemistry, Structural, Clinical, Workflow, Orchestrator, Discovery\u003C/li\u003E\u003Cli\u003E4 Cortex Analyst semantic views: Clinical, Genomics, Compounds, Chemistry\u003C/li\u003E\u003Cli\u003EReference data across 5 domains: Genomics (HGNC), ChEMBL (bioactivity), Clinical (ClinicalTrials.gov), Pathways (Reactome, MSigDB, GO), Protein (UniProt)\u003C/li\u003E\u003Cli\u003EA React web application deployed on Snowflake App Runtime\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EEnvironment Setup\u003C/h2\u003E\n","\u003Ch3\u003EClone the Repository\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sf-hcls-solutions.git\ncd sf-hcls-solutions/solutions/scientific-workbench\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EInstall Snowflake CLI\u003C/h3\u003E\n","\u003Cp\u003EIf you haven't already, install the Snowflake CLI:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install snowflake-cli\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure a Connection\u003C/h3\u003E\n","\u003Cp\u003EAdd a named connection for deployment. You need ACCOUNTADMIN role for the initial setup:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection add \\\n  --connection-name workbench-deploy \\\n  --account <your-account> \\\n  --user <your-user> \\\n  --role ACCOUNTADMIN \\\n  --warehouse COMPUTE_WH\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest the connection:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection test --connection workbench-deploy\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EObtain NVIDIA API Key\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EGo to \u003Ca href=\"https://build.nvidia.com\"\u003Ebuild.nvidia.com\u003C/a\u003E and create an account\u003C/li\u003E\u003Cli\u003ENavigate to any BioNeMo NIM (e.g., Boltz-2) and click "Get API Key"\u003C/li\u003E\u003Cli\u003ECopy the key — you'll provide it during deployment\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EQuick Start (One Command)\u003C/h3\u003E\n","\u003Cp\u003EThe fastest path is the automated deploy script:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ebash deploy.sh --connection workbench-deploy --nvidia-key <your-nvidia-key>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis runs all 7 phases automatically. If you prefer step-by-step deployment, follow Steps 3 through 7 below. If you used the one-command deploy, skip to Step 8.\u003C/p\u003E\n","\u003Cp\u003EFor non-interactive environments (CI, automation):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003ESWB_ASSUME_YES=1 bash deploy.sh --connection workbench-deploy --nvidia-key <your-nvidia-key>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfiguration File (Optional)\u003C/h3\u003E\n","\u003Cp\u003EFor repeatable deployments or custom naming, create a config file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecp workbench.config.yaml.example workbench.config.yaml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe config file controls all deployment parameters. Every field is optional — deploy.sh auto-detects defaults if omitted. CLI flags always take precedence over config values.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Esnowflake:\n  # Snowflake CLI connection name from ~/.snowflake/config.toml\n  connection: default\n\nsecrets:\n  # NVIDIA API key from build.nvidia.com (for hosted NIM API calls)\n  nvidia_api_key: ""\n  # NGC API key from ngc.nvidia.com (for SPCS NIM container weight downloads)\n  ngc_api_key: ""\n\ndatabases:\n  workbench: SCIENTIFIC_WORKBENCH      # Platform database\n  reference: WORKBENCH_REFERENCE       # Read-only reference data\n  projects: WORKBENCH_PROJECTS         # Per-project schemas\n\nwarehouses:\n  xs: WORKBENCH_XS                     # Lightweight queries, admin tasks\n  small: WORKBENCH_S                   # Tool execution, data loading\n  ml: WORKBENCH_ML                     # Heavy ML workloads, large data scans\n\n# Enable specific NIM containers on SPCS GPU compute pools\nnim_services:\n  boltz2: false                        # L40S GPU required\n  genmol: false                        # A10G GPU sufficient\n  diffdock: false                      # L40S GPU required\n  rfdiffusion: false                   # L40S GPU required\n  proteinmpnn: false                   # A10G GPU sufficient\n  molmim: false                        # A10G GPU sufficient\n  openfold2: false                     # A10G GPU sufficient\n  openfold3: false                     # L40S GPU required\n  msa_search: false                    # A10G + 500 GiB block volume\n\nagents:\n  deploy_legacy_sql: true              # Deploy agents via SQL scripts\n  deploy_agent_studio: true            # Deploy agents via Agent Studio YAML\n\nmarketplace:\n  pubmed_cke: false                    # Set true if PubMed CKE is subscribed\n  clinicaltrials_cke: false            # Set true if ClinicalTrials.gov CKE is subscribed\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDeploy with the config file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ebash deploy.sh --config workbench.config.yaml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EValidate the config before deploying:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ebash scripts/validate-config.sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Edeploy.sh Reference\u003C/h3\u003E\n","\u003Cp\u003EThe deploy script runs 7 phases in sequence. Use flags to skip or isolate phases:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFlag\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EEffect\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--connection NAME\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake CLI connection name (auto-detected if omitted)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--config PATH\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPath to \u003Ccode\u003Eworkbench.config.yaml\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--nvidia-key KEY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENVIDIA API key (or \u003Ccode\u003ENVIDIA_API_KEY\u003C/code\u003E env var)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--ngc-key KEY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENGC API key for SPCS NIMs (or \u003Ccode\u003ENGC_API_KEY\u003C/code\u003E env var)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--skip-prereqs\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESkip Phase 1 EAI/secret creation (if already done)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--skip-data\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESkip Phase 2 reference data loading (~45 min saved)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--skip-app\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESkip Phase 7 app deployment\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--skip-tests\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESkip post-deploy verification\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--backend-only\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDeploy only NVIDIA procedures, agents, and app\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--agents-app-only\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EResume at agents + app (skips infrastructure and data)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--app-only\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDeploy only the SAR app (fastest redeploy)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--mirror-nims\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMirror NIM container images from nvcr.io (requires Docker + \u003Ccode\u003E--ngc-key\u003C/code\u003E)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--dry-run\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPrint what would be executed without running\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDeployment phases:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPhase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EName\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EScripts\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDuration\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInfrastructure\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E00-prerequisites.sql\u003C/code\u003E through \u003Ccode\u003E13-execute-tool-by-name.sql\u003C/code\u003E (11 scripts)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2-5 min\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1b\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENIM Mirroring (opt-in)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDocker pull/tag/push of 9 NIM images\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E60-120 min\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReference Data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edata/loaders/*.sql\u003C/code\u003E, \u003Ccode\u003Edata/synthetic/load_synthetic_data.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E30-45 min\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETools\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etools/native/*.sql\u003C/code\u003E, \u003Ccode\u003Etools/nvidia/*.sql\u003C/code\u003E, \u003Ccode\u003Etools/search/*.sql\u003C/code\u003E, \u003Ccode\u003E14-custom-tool-onboarding.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5-10 min\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3.5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENotebooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUpload \u003Ccode\u003Enotebooks/*.ipynb\u003C/code\u003E to workspace\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1-2 min\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgents\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eengine/sql/agents/*.sql\u003C/code\u003E, Agent Studio YAML specs\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3-5 min\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic Views\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edata/semantic_views/*.sql\u003C/code\u003E, workflow runner\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2-3 min\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E6\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EApp Deployment\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnow app deploy\u003C/code\u003E (Docker build + SPCS service)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5-8 min\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E7\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVerification\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etests/*.sql\u003C/code\u003E, \u003Ccode\u003ECALL RUN_VALIDATION_TESTS()\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1-2 min\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETotal fresh deployment: ~50-80 minutes\u003C/strong\u003E (without NIM mirroring). Subsequent deploys with \u003Ccode\u003E--app-only\u003C/code\u003E take 5-8 minutes.\u003C/p\u003E\n","\u003Ch3\u003ETeardown\u003C/h3\u003E\n","\u003Cp\u003ETo remove the entire deployment:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ebash scripts/teardown.sh --connection workbench-deploy -y\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFlag\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EEffect\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E-y\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESkip confirmation prompt\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--dry-run\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPrint what would be dropped without executing\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E--keep-data\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDrop platform objects but preserve WORKBENCH_REFERENCE data\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch2\u003EDeploy Infrastructure\u003C/h2\u003E\n","\u003Cp\u003EThis step creates the Snowflake objects that the workbench runs on: databases, schemas, warehouses, compute pools, RBAC roles, external access integrations, and secrets.\u003C/p\u003E\n","\u003Ch3\u003EPhase 1: Run Infrastructure Scripts\u003C/h3\u003E\n","\u003Cp\u003EIf deploying manually (not using deploy.sh), execute these scripts in order using Snowsight or the Snowflake CLI:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EOrder\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EScript\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Creates\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Escripts/00-prerequisites.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExternal access integrations (NVIDIA API, PDB, NIM runtime), secrets, network rules\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Escripts/01-databases-and-schemas.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESCIENTIFIC_WORKBENCH (6 schemas), WORKBENCH_REFERENCE (5 schemas), WORKBENCH_PROJECTS\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Escripts/02-warehouses.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_XS, WORKBENCH_S, WORKBENCH_ML warehouses; NIM_GPU_A10G_POOL, NIM_GPU_L40S_POOL compute pools\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Escripts/03-rbac.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_ADMIN, WORKBENCH_SCIENTIST, WORKBENCH_VIEWER roles with appropriate grants\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Escripts/05-tool-registry.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECATALOG.TOOLS table, CATALOG.ASSETS table, CATALOG.CHAT_SESSIONS table, REGISTER_TOOL and SEED_ASSETS procedures\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EDatabase Layout\u003C/h3\u003E\n","\u003Cp\u003EAfter Phase 1, you have three databases:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESCIENTIFIC_WORKBENCH          -- Platform database\n  CATALOG                     -- Tool registry, assets, agents, chat sessions\n  WORKFLOWS                   -- Workflow templates and run history\n  GOVERNANCE                  -- Promotion log, canary assertions\n  PROVENANCE                  -- Provenance audit trail\n  PROJECTS                    -- Shared project space\n\nWORKBENCH_REFERENCE           -- Read-only reference data\n  GENOMICS                    -- HGNC gene nomenclature\n  CHEMBL                      -- Bioactivity measurements\n  CLINICAL                    -- ClinicalTrials.gov\n  PATHWAYS                    -- Reactome, MSigDB, GO\n  PROTEIN                     -- UniProt entries\n\nWORKBENCH_PROJECTS            -- Per-project schemas\n  DEMO_NSCLC                  -- NSCLC patient cohort demo\n  DEMO_COMPOUNDS              -- Compound screening demo\n  DEMO_CLINICAL               -- Clinical trials demo\n  DEMO_STRUCTURAL             -- Protein targets demo\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERBAC Roles\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERole\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETypical User\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_ADMIN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull access: manage tools, approve submissions, configure agents, share data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPlatform administrators\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_SCIENTIST\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERun tools, execute workflows, view all data, share results\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EComputational biologists, chemists\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_VIEWER\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERead-only access to results and catalog\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EManagers, reviewers\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EVerify Infrastructure\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check databases exist\nSHOW DATABASES LIKE 'SCIENTIFIC_WORKBENCH';\nSHOW DATABASES LIKE 'WORKBENCH_REFERENCE';\nSHOW DATABASES LIKE 'WORKBENCH_PROJECTS';\n\n-- Check roles\nSHOW ROLES LIKE 'WORKBENCH_%';\n\n-- Check external access integrations\nSHOW EXTERNAL ACCESS INTEGRATIONS LIKE 'NVIDIA_%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EDeploy NIMs on SPCS\u003C/h2\u003E\n","\u003Cp\u003EThis optional step deploys NVIDIA BioNeMo NIM containers locally on Snowpark Container Services (SPCS) GPU compute pools. By default, all NIM tools call the NVIDIA hosted API — SPCS deployment provides an alternative for customers who need air-gapped operation or want to avoid per-call API costs.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites for SPCS NIMs\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EDocker installed locally (for image mirroring)\u003C/li\u003E\u003Cli\u003ENGC API key from \u003Ca href=\"https://ngc.nvidia.com\"\u003Engc.nvidia.com\u003C/a\u003E (separate from the build.nvidia.com key)\u003C/li\u003E\u003Cli\u003EGPU compute pool quotas enabled in your account (A10G and/or L40S)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EGPU Requirements\u003C/h3\u003E\n","\u003Cp\u003EEach NIM has specific GPU and storage requirements. SPCS nodes have a 93.13 GiB storage cap across all instance families.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ENIM\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EContainer Image\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECompressed Size\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EGPU\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMemory\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStatus\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBoltz-2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/mit/boltz2:1.8.0\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E9.93 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EL40S 48 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E32-90 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVerified\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGenMol\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/nvidia/genmol:latest\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E9.44 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA10G 24 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8-24 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETemplate\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDiffDock\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/mit/diffdock:latest\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E15.33 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EL40S 48 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E32-64 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETemplate\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERFdiffusion\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/nvidia/rfdiffusion:2.3.0\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E16.37 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EL40S 48 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E32-90 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETemplate\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProteinMPNN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/nvidia/proteinmpnn:latest\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8.60 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA10G 24 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8-24 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETemplate\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMolMIM\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/nvidia/molmim:latest\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E13.04 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA10G 24 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E16-48 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETemplate\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpenFold2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/nvidia/openfold2:latest\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8-10 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA10G 24 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E16-48 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETemplate\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpenFold3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/nvidia/openfold3:latest\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10.19 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EL40S 48 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E32-90 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETemplate\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMSA-Search\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/nvidia/msa-search:latest\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8.64 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA10G 24 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E16-48 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESpecial (needs block volume)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEvo2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Envcr.io/nim/nvidia/evo2:latest\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E15.25 GiB\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EH100/H200\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETBD\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBlocked (needs measurement)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ECompute Pools\u003C/h3\u003E\n","\u003Cp\u003EThe infrastructure scripts (Step 3) create two GPU compute pools:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Already created by 02-warehouses.sql:\n-- NIM_GPU_A10G_POOL: GPU_NV_S (A10G 24 GiB) — GenMol, ProteinMPNN, MolMIM, OpenFold2\n-- NIM_GPU_L40S_POOL: GPU_L40S_G1_16 (L40S 48 GiB) — Boltz-2, DiffDock, RFdiffusion, OpenFold3\n\n-- Verify pools exist\nSHOW COMPUTE POOLS LIKE 'NIM_GPU_%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 1: Mirror NIM Images\u003C/h3\u003E\n","\u003Cp\u003ENIM containers must be mirrored from NVIDIA's NGC registry (\u003Ccode\u003Envcr.io\u003C/code\u003E) to your Snowflake image repository. This is required because SPCS pulls images from the Snowflake registry, not directly from external registries.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAutomated (via deploy.sh):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ebash deploy.sh --connection workbench-deploy \\\n  --nvidia-key <your-nvidia-key> \\\n  --ngc-key <your-ngc-key> \\\n  --mirror-nims\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EManual (per image):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Login to NGC registry\ndocker login nvcr.io -u '$oauthtoken' -p <your-ngc-key>\n\n# Login to Snowflake image registry\nsnow spcs image-registry login --connection workbench-deploy\n\n# Get your Snowflake registry URL\nREPO_URL=$(snow sql --connection workbench-deploy \\\n  --query "SHOW IMAGE REPOSITORIES IN SCHEMA SCIENTIFIC_WORKBENCH.CATALOG" \\\n  --format json | python3 -c "\nimport json, sys\ndata = json.load(sys.stdin)\nprint([r['repository_url'] for r in data if 'nim_gpu_images' in r.get('repository_url','').lower()][0])\n")\n\n# Mirror Boltz-2 (example — repeat for each NIM)\ndocker pull nvcr.io/nim/mit/boltz2:1.8.0\ndocker tag nvcr.io/nim/mit/boltz2:1.8.0 $REPO_URL/boltz2:1.8.0\ndocker push $REPO_URL/boltz2:1.8.0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant — Apple Silicon users:\u003C/strong\u003E Docker on ARM Macs pulls the \u003Ccode\u003Earm64\u003C/code\u003E variant by default. SPCS requires \u003Ccode\u003Eamd64\u003C/code\u003E. Pull by digest or use \u003Ccode\u003E--platform linux/amd64\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker pull --platform linux/amd64 nvcr.io/nim/mit/boltz2:1.8.0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EMirroring all 9 images takes 60-120 minutes depending on bandwidth.\u003C/p\u003E\n","\u003Ch3\u003EStep 2: Create SPCS Services\u003C/h3\u003E\n","\u003Cp\u003EThe service definitions are in \u003Ccode\u003Escripts/10-nim-spcs-services.sql\u003C/code\u003E. Only Boltz-2 is uncommented (verified). To deploy it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE SCIENTIFIC_WORKBENCH;\nUSE SCHEMA CATALOG;\n\nCREATE SERVICE SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC\n  IN COMPUTE POOL NIM_GPU_L40S_POOL\n  FROM SPECIFICATION $$\nspec:\n  containers:\n    - name: boltz2\n      image: /SCIENTIFIC_WORKBENCH/CATALOG/NIM_GPU_IMAGES/boltz2:1.8.0\n      env:\n        NIM_HTTP_API_PORT: "8000"\n        NIM_LOG_LEVEL: "INFO"\n      secrets:\n        - snowflakeSecret: SCIENTIFIC_WORKBENCH.CATALOG.NGC_API_KEY\n          secretKeyRef: SECRET_STRING\n          envVarName: NGC_API_KEY\n      volumeMounts:\n        - name: dshm\n          mountPath: /dev/shm\n      resources:\n        requests:\n          nvidia.com/gpu: 1\n          memory: 32Gi\n        limits:\n          nvidia.com/gpu: 1\n          memory: 90Gi\n      readinessProbe:\n        port: 8000\n        path: /v1/health/ready\n  endpoints:\n    - name: boltz2\n      port: 8000\n      public: true\n  volumes:\n    - name: dshm\n      source: memory\n      size: 16Gi\n  $$\n  EXTERNAL_ACCESS_INTEGRATIONS = (NIM_RUNTIME_EAI)\n  MIN_INSTANCES = 1\n  MAX_INSTANCES = 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKey points about the service spec:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003E/dev/shm\u003C/code\u003E volume\u003C/strong\u003E: All PyTorch-based NIM containers require a shared memory volume. SPCS uses \u003Ccode\u003Esource: memory\u003C/code\u003E for this.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ENGC_API_KEY\u003C/code\u003E secret\u003C/strong\u003E: Mounted as an environment variable for model weight downloads on first startup.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EReadiness probe\u003C/strong\u003E: \u003Ccode\u003E/v1/health/ready\u003C/code\u003E — the service reports READY only after weights are loaded.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStartup time\u003C/strong\u003E: ~7.5 minutes (3.5 min image pull + 4 min weight download). Add 11-15 minutes if the compute pool must provision a new node.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 3: Verify Service Health\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check service status\nSELECT SYSTEM$GET_SERVICE_STATUS('SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC');\n\n-- View container logs\nSELECT SYSTEM$GET_SERVICE_LOGS('SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC', 0, 'boltz2', 50);\n\n-- Check endpoint URL\nSHOW ENDPOINTS IN SERVICE SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEndpoint Path Reference\u003C/h3\u003E\n","\u003Cp\u003EThe hosted API and self-hosted containers use different URL structures. For the hosted API, the base is \u003Ccode\u003Ehttps://health.api.nvidia.com\u003C/code\u003E. For self-hosted containers (including SPCS), the base is \u003Ccode\u003Ehttp://localhost:8000\u003C/code\u003E (or the SPCS service DNS).\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ENIM\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EHosted API Path\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESelf-Hosted Container Path\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDocs\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EBoltz-2\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/mit/boltz2/predict\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/mit/boltz2/predict\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/mit/boltz2\"\u003Ebuild.nvidia.com/boltz2\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EGenMol\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/nvidia/genmol/generate\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/nvidia/genmol/generate\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/nvidia/genmol\"\u003Ebuild.nvidia.com/genmol\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDiffDock\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/mit/diffdock\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/mit/diffdock\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/mit/diffdock\"\u003Ebuild.nvidia.com/diffdock\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERFdiffusion\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/ipd/rfdiffusion/generate\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/ipd/rfdiffusion/generate\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/ipd/rfdiffusion\"\u003Ebuild.nvidia.com/rfdiffusion\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EProteinMPNN\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/ipd/proteinmpnn/predict\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/ipd/proteinmpnn/predict\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/ipd/proteinmpnn\"\u003Ebuild.nvidia.com/proteinmpnn\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EMolMIM\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/nvidia/molmim/generate\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/nvidia/molmim/generate\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/nvidia/molmim\"\u003Ebuild.nvidia.com/molmim\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EOpenFold2\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/openfold/openfold2/predict-structure-from-msa-and-template\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/openfold/openfold2/predict-structure-from-msa-and-template\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/openfold/openfold2\"\u003Ebuild.nvidia.com/openfold2\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EOpenFold3\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/openfold/openfold3/predict\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/openfold/openfold3/predict\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/openfold/openfold3\"\u003Ebuild.nvidia.com/openfold3\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EMSA-Search\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/colabfold/msa-search/predict\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/colabfold/msa-search/predict\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/colabfold/msa-search\"\u003Ebuild.nvidia.com/msa-search\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EEvo2\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/v1/biology/arc/evo2-40b/generate\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/biology/arc/evo2-40b/generate\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://build.nvidia.com/arc/evo2-40b\"\u003Ebuild.nvidia.com/evo2\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe pattern: the self-hosted container path is the hosted path minus the \u003Ccode\u003E/v1\u003C/code\u003E prefix. The hosted API requires a Bearer token (\u003Ccode\u003EAuthorization: Bearer <NVIDIA_API_KEY>\u003C/code\u003E); self-hosted containers do not require authentication.\u003C/p\u003E\n","\u003Cp\u003ETo verify a container's actual endpoint after deployment, query its OpenAPI spec:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl http://<service-endpoint>:8000/openapi.json | python3 -m json.tool | grep '"paths"' -A 20\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECost Management\u003C/h3\u003E\n","\u003Cp\u003ESPCS GPU services consume credits continuously while running. To manage costs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Suspend a service (stops GPU billing)\nALTER SERVICE SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC SUSPEND;\n\n-- Resume when needed\nALTER SERVICE SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe compute pools have auto-suspend configured (300 seconds by default). However, auto-suspend only triggers after all services in the pool are suspended. Suspend services explicitly when not in use.\u003C/p\u003E\n","\u003Ch3\u003ESpecial Cases\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EMSA-Search\u003C/strong\u003E: Requires a 500 GiB+ block volume for reference sequence databases (~1.2 TB for full UniRef30/ColabFold). The service spec includes a \u003Ccode\u003Eblock\u003C/code\u003E volume sourced from a stage.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEvo2\u003C/strong\u003E: The 40B parameter model's runtime footprint (~89 GiB) is close to the 93.13 GiB node storage cap. Do not deploy until measured on your target instance family. May require H100 80 GiB or H200 141 GiB VRAM.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ELoad Reference Data\u003C/h2\u003E\n","\u003Cp\u003EPhase 2 loads ~40 GB of reference data across 5 scientific domains. This is the longest phase (~30-45 minutes).\u003C/p\u003E\n","\u003Ch3\u003EPhase 2: Reference Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# If deploying manually, run these scripts:\n# scripts/04-reference-data.sql (schema setup)\n# data/loaders/load_chembl.sql\n# data/loaders/load_remaining.sql\n# data/synthetic/load_synthetic_data.sql\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EData Domains\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDomain\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESchema\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EKey Tables\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESource\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERows (approx)\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGenomics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGENOMICS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHGNC_GENES\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHGNC\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E43,000\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBioactivity\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECHEMBL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACTIVITIES, ASSAYS, MOLECULE_DICTIONARY, TARGET_DICTIONARY\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChEMBL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2M+\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClinical\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECLINICAL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESTUDIES, CONDITIONS, INTERVENTIONS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClinicalTrials.gov\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E500K+\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPathways\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPATHWAYS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPATHWAYS, PATHWAY_GENES, GENE_SETS, GENE_SET_MEMBERS, GO_ANNOTATIONS, GO_TERMS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReactome, MSigDB, GO\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1M+\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProtein\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPROTEIN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUNIPROT_ENTRIES\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUniProt\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E570K+\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EVerify Data Loading\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check row counts across reference schemas\nSELECT TABLE_SCHEMA, TABLE_NAME, ROW_COUNT\nFROM WORKBENCH_REFERENCE.INFORMATION_SCHEMA.TABLES\nWHERE TABLE_SCHEMA IN ('GENOMICS', 'CHEMBL', 'CLINICAL', 'PATHWAYS', 'PROTEIN')\n  AND TABLE_TYPE = 'BASE TABLE'\nORDER BY TABLE_SCHEMA, TABLE_NAME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ERegister Tools\u003C/h2\u003E\n","\u003Cp\u003EPhase 3 creates the 28 agent-callable tool procedures and registers them in the CATALOG.TOOLS table.\u003C/p\u003E\n","\u003Ch3\u003ETool Taxonomy\u003C/h3\u003E\n","\u003Cp\u003EThe workbench supports four types of tools:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERuntime\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECount\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENative\u003C/strong\u003E (Snowpark Python)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Evalidate_molecule\u003C/code\u003E, \u003Ccode\u003Erun_differential_expression\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E7\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENIM Wrapper\u003C/strong\u003E (NVIDIA API)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarehouse + NVIDIA hosted API\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_boltz2\u003C/code\u003E, \u003Ccode\u003Erun_genmol\u003C/code\u003E, \u003Ccode\u003Erun_diffdock\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E11\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESearch\u003C/strong\u003E (Cortex Search / CKE)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Search service\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esearch_pubmed\u003C/code\u003E, \u003Ccode\u003Esearch_clinical_trials\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EPipeline\u003C/strong\u003E (multi-tool chain)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_drug_discovery_pipeline\u003C/code\u003E, \u003Ccode\u003Erun_msa_structure_pipeline\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EImaging\u003C/strong\u003E (MONAI NIM)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarehouse + NVIDIA hosted API\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_monai_vista3d\u003C/code\u003E, \u003Ccode\u003Erun_monai_lung_nodule\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EGenomics\u003C/strong\u003E (Parabricks)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESPCS GPU (pending)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_parabricks_fq2bam\u003C/code\u003E, \u003Ccode\u003Erun_parabricks_deepvariant\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EHow Tools Are Structured\u003C/h3\u003E\n","\u003Cp\u003EEvery tool is a Snowflake stored procedure or function with a JSON annotation in its COMMENT field:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE PROCEDURE CATALOG.MY_TOOL(...)\nRETURNS VARCHAR\nLANGUAGE PYTHON\n...\nCOMMENT = 'TOOL:{"display_name":"My Tool","description":"...","domains":"genomics","params":{...},"return_type":"VARCHAR","example":"CALL CATALOG.MY_TOOL(...)"}'\nAS $$\n...\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EREGISTER_TOOL\u003C/code\u003E procedure adds the tool to \u003Ccode\u003ECATALOG.TOOLS\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL CATALOG.REGISTER_TOOL(\n  'my_tool',                                    -- name (unique)\n  'My Tool',                                    -- display_name\n  'Description of what the tool does.',         -- description\n  'genomics,drug-discovery',                    -- domains (comma-separated)\n  'procedure',                                  -- tool_type\n  'SCIENTIFIC_WORKBENCH.CATALOG.MY_TOOL',       -- function_reference\n  '{"param1":"STRING","param2":"INT"}',         -- parameters (JSON)\n  'VARCHAR',                                    -- return_type\n  'CALL CATALOG.MY_TOOL(''value'', 42)'         -- example_usage\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ENVIDIA BioNeMo NIM Tools\u003C/h3\u003E\n","\u003Cp\u003EThese 11 tools call the NVIDIA hosted API via the \u003Ccode\u003ENVIDIA_API_EAI\u003C/code\u003E external access integration:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETool\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ENIM Model\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_boltz2\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBoltz-2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProtein structure prediction + binding affinity (pIC50)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_genmol\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGenMol\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDe novo drug-like molecule generation\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_diffdock\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDiffDock\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESmall-molecule docking (binding pose prediction)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_proteinmpnn\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProteinMPNN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInverse folding (backbone to sequence design)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_rfdiffusion\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERFdiffusion\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDe novo protein backbone generation\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_molmim\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMolMIM\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELatent-space molecule optimization\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_openfold2\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpenFold2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESingle-chain protein structure prediction\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_openfold3\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpenFold3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMulti-chain complex structure prediction\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_msa_search\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMSA-Search\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMultiple sequence alignment via ColabFold\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_evo2\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEvo2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDNA/RNA sequence generation (40B parameter model)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erun_drug_discovery_pipeline\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGenMol + DiffDock\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnd-to-end: generate, validate, dock, rank\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EVerify Tool Registration\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT tool_type, COUNT(*) AS count\nFROM SCIENTIFIC_WORKBENCH.CATALOG.TOOLS\nWHERE status = 'active'\nGROUP BY tool_type\nORDER BY count DESC;\n\n-- Expected: 28 total active tools\nSELECT COUNT(*) AS total_active_tools\nFROM SCIENTIFIC_WORKBENCH.CATALOG.TOOLS\nWHERE status = 'active';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConfigure AI Services\u003C/h2\u003E\n","\u003Cp\u003EPhase 4-6 sets up Cortex Search services, the governance framework, workflow engine, semantic views, and the agent layer.\u003C/p\u003E\n","\u003Ch3\u003ECortex Search and Governance\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EScript\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Creates\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Escripts/06-cortex-services.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Search services for tool discovery and asset catalog\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Escripts/07-governance.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGOVERNANCE.PROMOTION_LOG, GOVERNANCE.CANARY_ASSERTIONS\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Escripts/08-workflow-engine.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKFLOWS.TEMPLATES, WORKFLOWS.RUNS, workflow execution procedures\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ESemantic Views (Cortex Analyst)\u003C/h3\u003E\n","\u003Cp\u003EFour semantic views enable natural-language queries over structured data:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESemantic View\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EUnderlying Data\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample Query\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESV_CLINICAL\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDEMO_NSCLC patient cohort\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"How many patients are in stage IIIA?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESV_GENOMICS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGene expression + pathways\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Which genes are upregulated in the NSCLC cohort?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESV_COMPOUNDS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompound IC50 assay results\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Show me compounds with IC50 below 100 nM against EGFR"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESV_CHEMISTRY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChEMBL molecule reference\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"List all approved kinase inhibitors"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EAgent Architecture\u003C/h3\u003E\n","\u003Cp\u003EThe workbench uses a multi-agent architecture with specialized domain agents coordinated by an orchestrator:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E                    DISCOVERY_AGENT\n                    (user-facing)\n                         |\n               uses semantic views +\n               search + code execution\n                         |\n                  ORCHESTRATOR_AGENT\n                  (routes to domains)\n                   /     |     \\\n        GENOMICS   CHEMISTRY  STRUCTURAL\n         AGENT      AGENT      AGENT\n           |          |          |\n        5 tools    4 tools    9 tools\n                         |\n                   CLINICAL_AGENT\n                   (4 semantic views)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach domain agent has access only to its relevant tools. The ORCHESTRATOR_AGENT routes queries to the correct domain and synthesizes cross-domain results. The DISCOVERY_AGENT is the user-facing agent that also has direct access to semantic views and code execution.\u003C/p\u003E\n","\u003Ch3\u003EVerify Agent Configuration\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Test the Discovery Agent with a simple query\nSELECT SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n  'SCIENTIFIC_WORKBENCH.CATALOG.DISCOVERY_AGENT',\n  '{"query": "What tools are available for protein structure prediction?"}',\n  TRUE\n) AS response;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EDeploy the Application\u003C/h2\u003E\n","\u003Cp\u003EPhase 7 deploys the React web application to Snowflake App Runtime (SAR).\u003C/p\u003E\n","\u003Ch3\u003ESAR Deployment\u003C/h3\u003E\n","\u003Cp\u003EFrom the \u003Ccode\u003Eapp/\u003C/code\u003E directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd solutions/scientific-workbench/app\nsnow app deploy --connection workbench-deploy\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis performs three steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EUploads source files to a Snowflake workspace\u003C/li\u003E\u003Cli\u003EBuilds a Docker container in the cloud (via SPCS build service)\u003C/li\u003E\u003Cli\u003ECreates or upgrades the APPLICATION SERVICE\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EBuild time is approximately 3-5 minutes. The output includes the app URL:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EApp ready at https://<hash>-<account>.snowflakecomputing.app\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGrant Access to Users\u003C/h3\u003E\n","\u003Cp\u003EAfter deployment, grant access to the appropriate roles:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Scientists can use the app\nGRANT USAGE ON APPLICATION SERVICE SCIENTIFIC_WORKBENCH_APP\n  TO ROLE WORKBENCH_SCIENTIST;\n\n-- Viewers get read-only access\nGRANT USAGE ON APPLICATION SERVICE SCIENTIFIC_WORKBENCH_APP\n  TO ROLE WORKBENCH_VIEWER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EApp Modules\u003C/h3\u003E\n","\u003Cp\u003EThe application includes 10 modules:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EModule\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPath\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHome\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDashboard with tool count, agent status, recent activity\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChat\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/chat\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConversational interface to the Discovery Agent\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExplore\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/explore\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESchema browser with column profiling and data preview\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExperiments\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/experiments\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWorkflow run history and results viewer\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAsset Catalog\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/catalog\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESearchable catalog of all platform assets with data preview\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETools\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/tools\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERegistry of all 28 agent-callable tools\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool Onboard\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/tools/onboard\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustom tool submission wizard\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGovernance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/governance\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPromotion log, canary assertions, provenance trail\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENotebooks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/notebooks\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake notebook launcher\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EShare\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E/share\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECross-account data sharing (admin/scientist)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EVerify Deployment\u003C/h3\u003E\n","\u003Cp\u003EOpen the app URL in your browser. You should see:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHome page with "28 Active Tools" (or similar count)\u003C/li\u003E\u003Cli\u003EChat page where you can talk to the Discovery Agent\u003C/li\u003E\u003Cli\u003ETools page listing all registered tools\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ELocal Development\u003C/h2\u003E\n","\u003Cp\u003EFor development and testing, you can run the app locally against your Snowflake account.\u003C/p\u003E\n","\u003Ch3\u003ESetup\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd solutions/scientific-workbench/app\nnpm install\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEnvironment Variables\u003C/h3\u003E\n","\u003Cp\u003ECreate a \u003Ccode\u003E.env.local\u003C/code\u003E file (or rely on \u003Ccode\u003E~/.snowflake/config.toml\u003C/code\u003E default connection):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Option 1: Key-pair auth (recommended)\nSNOWFLAKE_ACCOUNT=<account>\nSNOWFLAKE_USER=<user>\nSNOWFLAKE_PRIVATE_KEY_PATH=~/.snowflake/rsa_key.p8\nSNOWFLAKE_WAREHOUSE=WORKBENCH_XS\nSNOWFLAKE_ROLE=WORKBENCH_ADMIN\n\n# Option 2: config.toml (zero config — uses default connection)\n# No env vars needed if ~/.snowflake/config.toml is configured\n\n# Enable admin features in local dev\nSWB_LOCAL_DEV_ADMIN=true\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun the Dev Server\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Enpm run dev\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe app starts at \u003Ccode\u003Ehttp://localhost:3000\u003C/code\u003E. Hot reload is enabled — changes to pages and API routes take effect immediately.\u003C/p\u003E\n","\u003Ch3\u003EKey Differences from SPCS\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAspect\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELocal Dev\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESPCS (Deployed)\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAuth\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPassword or config.toml\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESPCS service token\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECaller's rights\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENot available (use SWB_LOCAL_DEV_ADMIN)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReads sf-context-current-user-token header\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EURL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Elocalhost:3000\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<hash>-<account>.snowflakecomputing.app\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESecrets\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnvironment variables\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EgetSecret()\u003C/code\u003E API from app.yml\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ETesting Changes\u003C/h3\u003E\n","\u003Cp\u003EBefore deploying, verify your changes build successfully:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Enpx tsc --noEmit       # Type checking\nnpx next build         # Full production build\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen deploy with \u003Ccode\u003Esnow app deploy --connection workbench-deploy\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch2\u003ERun a Discovery Workflow\u003C/h2\u003E\n","\u003Cp\u003ENow that the workbench is deployed, walk through an end-to-end drug discovery workflow.\u003C/p\u003E\n","\u003Ch3\u003EOpen the Chat\u003C/h3\u003E\n","\u003Cp\u003ENavigate to the Chat page in the web application. You'll see the Discovery Agent interface.\u003C/p\u003E\n","\u003Ch3\u003EExample: Multi-Step Drug Discovery\u003C/h3\u003E\n","\u003Cp\u003ETry this prompt:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EFind compounds in ChEMBL that target EGFR with IC50 below 100 nM, validate their drug-likeness, and predict binding poses for the top 3 candidates against PDB structure 1M17.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EQuery the \u003Ccode\u003ESV_COMPOUNDS\u003C/code\u003E semantic view to find EGFR compounds with IC50 < 100 nM\u003C/li\u003E\u003Cli\u003ECall \u003Ccode\u003Evalidate_molecule\u003C/code\u003E on each candidate (RDKit sanitization + PAINS/BRENK filters)\u003C/li\u003E\u003Cli\u003ECall \u003Ccode\u003Erun_diffdock\u003C/code\u003E to dock the top 3 validated molecules against PDB 1M17\u003C/li\u003E\u003Cli\u003EReturn structured results with confidence scores and binding poses\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EExample: Genomics Analysis\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ERun differential expression analysis comparing Stage I vs Stage III patients in the NSCLC cohort, then perform pathway enrichment on the upregulated genes.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECall \u003Ccode\u003Erun_differential_expression\u003C/code\u003E on the NSCLC gene expression data\u003C/li\u003E\u003Cli\u003EFilter for significantly upregulated genes (log2FC > 1, FDR < 0.05)\u003C/li\u003E\u003Cli\u003ECall \u003Ccode\u003Erun_pathway_enrichment\u003C/code\u003E against MSigDB Hallmark gene sets\u003C/li\u003E\u003Cli\u003EReturn enriched pathways with Fisher exact test p-values\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EExample: Protein Structure Prediction\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EPredict the structure of the first 200 residues of human TP53 protein and assess confidence.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELook up the TP53 sequence (or ask you to provide it)\u003C/li\u003E\u003Cli\u003ECall \u003Ccode\u003Erun_openfold2\u003C/code\u003E for single-chain structure prediction\u003C/li\u003E\u003Cli\u003EReturn the predicted structure with pLDDT confidence scores\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EView Results\u003C/h3\u003E\n","\u003Cp\u003EAfter each workflow, results are saved to output tables. View them in:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EExperiments\u003C/strong\u003E page — workflow run history with parameters and status\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExplore\u003C/strong\u003E page — browse the output table schema and preview rows\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAsset Catalog\u003C/strong\u003E — newly created result tables appear as assets\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EExtend the Platform\u003C/h2\u003E\n","\u003Ch3\u003EAdding a New Native Tool\u003C/h3\u003E\n","\u003Cp\u003ECreate a Snowpark Python procedure in \u003Ccode\u003ESCIENTIFIC_WORKBENCH.CATALOG\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE PROCEDURE CATALOG.MY_NEW_TOOL(\n    "P_INPUT" VARCHAR,\n    "P_OUTPUT_TABLE" VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.11'\nPACKAGES = ('snowflake-snowpark-python')\nHANDLER = 'run'\nCOMMENT = 'TOOL:{"display_name":"My New Tool","description":"What it does","domains":"genomics","params":{"input":"STRING","output_table":"STRING"},"return_type":"VARCHAR","example":"CALL CATALOG.MY_NEW_TOOL(input, output)"}'\nAS $$\nimport json\n\ndef run(session, p_input: str, p_output_table: str) -> str:\n    # Your tool logic here\n    result = {"status": "success", "output_table": p_output_table}\n    return json.dumps(result)\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERegister it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL CATALOG.REGISTER_TOOL(\n    'my_new_tool', 'My New Tool',\n    'What it does',\n    'genomics', 'procedure',\n    'SCIENTIFIC_WORKBENCH.CATALOG.MY_NEW_TOOL',\n    '{"input":"STRING","output_table":"STRING"}',\n    'VARCHAR',\n    'CALL CATALOG.MY_NEW_TOOL(''test'', ''RESULTS.OUTPUT'')'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERefresh the asset catalog:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL CATALOG.SEED_ASSETS();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAdding a New NIM Wrapper\u003C/h3\u003E\n","\u003Cp\u003EFollow the same pattern as existing NIM tools. Key requirements:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAdd \u003Ccode\u003EEXTERNAL_ACCESS_INTEGRATIONS = (NVIDIA_API_EAI)\u003C/code\u003E to the procedure\u003C/li\u003E\u003Cli\u003EAdd \u003Ccode\u003ESECRETS = ('nvidia_key' = SCIENTIFIC_WORKBENCH.CATALOG.NVIDIA_API_SECRET)\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EUse the \u003Ccode\u003E_snowflake.get_generic_secret_string('nvidia_key')\u003C/code\u003E API to read the key\u003C/li\u003E\u003Cli\u003ECall the NIM endpoint via \u003Ccode\u003Erequests.post()\u003C/code\u003E with Bearer token auth\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ECustom Tool Onboarding\u003C/h3\u003E\n","\u003Cp\u003EScientists can submit custom tools through the web application:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003ETools > Onboard\u003C/strong\u003E in the app\u003C/li\u003E\u003Cli\u003EFill in the tool specification (name, description, parameters, code)\u003C/li\u003E\u003Cli\u003ESubmit for review\u003C/li\u003E\u003Cli\u003EAn admin reviews and approves/rejects via \u003Cstrong\u003ETools > Submissions\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EApproved tools are automatically registered in the catalog\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EAdding Reference Data\u003C/h3\u003E\n","\u003Cp\u003ETo add a new reference dataset:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate a schema in WORKBENCH_REFERENCE (or use an existing one)\u003C/li\u003E\u003Cli\u003ELoad the data\u003C/li\u003E\u003Cli\u003ERegister it as an asset:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO SCIENTIFIC_WORKBENCH.CATALOG.ASSETS\n    (asset_id, asset_name, asset_type, description, domain, schema_name, owner)\nVALUES\n    ('asset-data-WORKBENCH_REFERENCE.MY_SCHEMA.MY_TABLE',\n     'MY_TABLE', 'dataset', 'Description of the dataset',\n     'MY_SCHEMA', 'MY_SCHEMA', CURRENT_USER());\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOr run \u003Ccode\u003ECALL CATALOG.SEED_ASSETS()\u003C/code\u003E to auto-discover tables in registered schemas.\u003C/p\u003E\n","\u003Ch2\u003ESecurity Model\u003C/h2\u003E\n","\u003Ch3\u003EPlatform Roles and Hierarchy\u003C/h3\u003E\n","\u003Cp\u003EThe workbench uses three Snowflake database roles in a hierarchical inheritance chain:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESYSADMIN\n  └── WORKBENCH_ADMIN       (full platform administration)\n        └── WORKBENCH_SCIENTIST   (run tools, execute workflows, share results)\n              └── WORKBENCH_VIEWER      (read-only access to results and catalog)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach higher role inherits all privileges of the roles below it. WORKBENCH_ADMIN inherits SCIENTIST, which inherits VIEWER.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERole\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECan Do\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECannot Do\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EWORKBENCH_ADMIN\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEverything: manage tools, approve custom tool submissions, configure agents, share data cross-account, manage SPCS services, run governance procedures\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EN/A (full access)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EWORKBENCH_SCIENTIST\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERun all tools, execute workflows, view all data, create result tables, share results, write to provenance log, submit custom tools\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EApprove/reject tool submissions, manage compute pools, run governance procedures\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EWORKBENCH_VIEWER\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERead catalog, view results, view tool registry, use XS warehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERun tools, execute workflows, create tables, share data\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EAccess Control by Schema\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDatabase.Schema\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EADMIN\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESCIENTIST\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EVIEWER\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESCIENTIFIC_WORKBENCH.CATALOG\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull DDL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESELECT + CALL procedures\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESELECT only\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESCIENTIFIC_WORKBENCH.RESULTS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull DDL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESELECT + INSERT + CREATE TABLE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESELECT only\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESCIENTIFIC_WORKBENCH.WORKFLOWS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull DDL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESELECT + CALL procedures\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo access\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESCIENTIFIC_WORKBENCH.GOVERNANCE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull DDL + procedures\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESELECT only\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo access\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESCIENTIFIC_WORKBENCH.PROVENANCE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull DDL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESELECT + INSERT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo access\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_REFERENCE.*\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull DDL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESELECT (read-only)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo access\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_PROJECTS.*\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull DDL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESELECT (read-only)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo access\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EScientific Personas\u003C/h3\u003E\n","\u003Cp\u003EIn a typical deployment, the platform roles map to scientific personas. Assign users to the appropriate role based on their function:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPersona\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESnowflake Role\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat They Do\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EComputational Biologist\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_SCIENTIST\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDifferential expression analysis, pathway enrichment, survival analysis, gene symbol validation\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EMedicinal Chemist\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_SCIENTIST\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMolecule generation (GenMol), optimization (MolMIM), validation (RDKit + PAINS), molecular descriptors\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EStructural Biologist\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_SCIENTIST\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProtein structure prediction (Boltz-2, OpenFold2/3), backbone design (RFdiffusion), sequence design (ProteinMPNN), molecular docking (DiffDock)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EClinical Data Scientist\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_SCIENTIST\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECohort queries via semantic views, clinical trial search, patient outcome analysis\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAI Drug Discovery Scientist\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_SCIENTIST\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnd-to-end pipelines, multi-tool workflows, agent-driven discovery\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EPlatform Administrator\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_ADMIN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool onboarding, agent configuration, data sharing, governance, compute management\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EManager / Reviewer\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_VIEWER\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReview experiment results, browse catalog, audit provenance\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EAssigning Roles to Users\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Grant a scientist role to a user\nGRANT ROLE WORKBENCH_SCIENTIST TO USER jane_doe;\n\n-- Grant admin role\nGRANT ROLE WORKBENCH_ADMIN TO USER platform_admin;\n\n-- Grant viewer role\nGRANT ROLE WORKBENCH_VIEWER TO USER manager_smith;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EUsers switch to their workbench role in a Snowflake session:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE WORKBENCH_SCIENTIST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn the web application, caller's rights automatically detects the user's active role via the SPCS user token — no manual \u003Ccode\u003EUSE ROLE\u003C/code\u003E needed.\u003C/p\u003E\n","\u003Ch3\u003ENamed Query Allowlist\u003C/h3\u003E\n","\u003Cp\u003EThe web application does not execute arbitrary SQL from the client. All queries go through a \u003Cstrong\u003Enamed query registry\u003C/strong\u003E (\u003Ccode\u003Elib/named-queries.ts\u003C/code\u003E) that maps query keys to pre-defined SQL templates:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStatic queries\u003C/strong\u003E (no parameters): SQL is a compile-time constant — zero attack surface\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EParameterized queries\u003C/strong\u003E: Parameters are validated with Zod schemas and used as bind variables (\u003Ccode\u003E?\u003C/code\u003E) — no string interpolation\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003EClient: { query: "column_info", params: { schema: "GENOMICS", table: "HGNC_GENES" } }\n                                    |\n                              resolveNamedQuery()\n                                    |\nServer: SELECT COLUMN_NAME, DATA_TYPE FROM INFORMATION_SCHEMA.COLUMNS\n        WHERE TABLE_SCHEMA = ? AND TABLE_NAME = ?\n        binds: ['GENOMICS', 'HGNC_GENES']\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAuthorization Patterns\u003C/h3\u003E\n","\u003Cp\u003EAPI routes use caller's rights to check the user's Snowflake role:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003E// Check caller's role via SPCS user token\nconst [row] = await querySnowflake("SELECT CURRENT_ROLE() AS role", { callersRights: true })\nconst callerRole = String(row?.ROLE ?? "").toUpperCase()\n\nif (!ALLOWED_ROLES.has(callerRole)) {\n  return Response.json({ error: "Forbidden" }, { status: 403 })\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EEndpoint\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERequired Role\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGET /api/tools/submissions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAny authenticated user\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPOST /api/tools/submissions (approve/reject)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_ADMIN\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPOST /api/share (grant/create)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKBENCH_ADMIN or WORKBENCH_SCIENTIST\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGET /api/chat/sessions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EScoped to CURRENT_USER()\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EExternal Access Integrations\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EEAI\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAllowed Hosts\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENVIDIA_API_EAI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENVIDIA BioNeMo NIM API calls\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eintegrate.api.nvidia.com\u003C/code\u003E, \u003Ccode\u003Eapi.nvcf.nvidia.com\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPDB_API_EAI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERCSB Protein Data Bank lookups\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edata.rcsb.org\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENIM_RUNTIME_EAI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESPCS NIM container weight downloads\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E*.nvidia.com\u003C/code\u003E, \u003Ccode\u003E*.nvcr.io\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've deployed a complete Scientific Workbench for life sciences R&D on Snowflake. The platform provides:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA multi-agent AI system with 28 tools spanning genomics, chemistry, structural biology, and clinical data\u003C/li\u003E\u003Cli\u003ENVIDIA BioNeMo NIM integration for state-of-the-art molecular and protein AI\u003C/li\u003E\u003Cli\u003EA governed, role-based environment with audit trails and provenance tracking\u003C/li\u003E\u003Cli\u003EA modern web application for interactive discovery workflows\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EDeploying a multi-database, multi-schema Snowflake platform with automated scripts\u003C/li\u003E\u003Cli\u003EConfiguring NVIDIA BioNeMo NIMs as agent-callable tools\u003C/li\u003E\u003Cli\u003ESetting up Cortex Agents with domain-specific tool routing\u003C/li\u003E\u003Cli\u003EBuilding and deploying a Snowflake App Runtime (Next.js) application\u003C/li\u003E\u003Cli\u003EExtending the platform with new tools, data, and semantic views\u003C/li\u003E\u003Cli\u003EImplementing security patterns: named query allowlists, RBAC, caller's rights\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ESnowflake Cortex Agents Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-app-runtime/about-snowflake-app-runtime\"\u003ESnowflake App Runtime Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.nvidia.com/en-us/clara/bionemo/\"\u003ENVIDIA BioNeMo Platform\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://build.nvidia.com/explore/healthcare\"\u003ENVIDIA NIM API Catalog\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst (Semantic Views)\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sf-hcls-solutions\"\u003ESource Repository\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Scientific Workbench for Life Sciences R&D",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nThe Scientific Workbench is an enterprise AI platform for life sciences research and development, built entirely on Snowflake. It brings together Cortex Agents, NVIDIA BioNeMo NIMs, Snowpark Python tools, and a Snowflake App Runtime (SAR) web application into a single governed environment where computational biologists, medicinal chemists, structural biologists, and clinical data scientists can run AI-driven discovery workflows.\n\nBy the end of this guide you will have a fully deployed Scientific Workbench with 28 agent-callable tools, a multi-agent Discovery Agent, 4 Cortex Analyst semantic views, reference datasets across 5 scientific domains, and a React web application — all running in your Snowflake account.\n\n![Architecture Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/scientific-workbench-for-life-sciences/architecture_diagram.png?v=cef43ec8)\n\n### Prerequisites\n\n- Snowflake account (Enterprise edition or higher recommended)\n- ACCOUNTADMIN access (for initial setup; can be reduced after deployment)\n- [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/index) (`snow`) installed\n- NVIDIA API key from [build.nvidia.com](https://build.nvidia.com) (free tier available)\n- Node.js 20+ and npm (for local development only)\n\n### What You'll Learn\n\n- How to deploy a multi-agent AI platform on Snowflake\n- How to configure NVIDIA BioNeMo NIM tools for drug discovery\n- How to set up role-based access control for scientific teams\n- How to build and deploy a Snowflake App Runtime (SAR) web application\n- How to run end-to-end drug discovery workflows through a conversational agent\n- How to extend the platform with new tools and data sources\n\n### What You'll Need\n\n| Resource | Details |\n|----------|---------|\n| Storage (reference data) | ~40 GB |\n| Storage (platform tables) | \u003C 1 GB |\n| Warehouses | 3 (XS, S, ML) with auto-suspend |\n| Cortex Search | 2 services (catalog + tools) |\n| Marketplace subscriptions | PubMed CKE, ClinicalTrials.gov CKE |\n| GPU compute | None required (NVIDIA hosted API) |\n| Monthly credits (estimate) | 500 - 1,000 |\n\n### What You'll Build\n\n- 28 agent-callable tools: 7 native Snowpark Python, 11 NVIDIA BioNeMo NIM wrappers, 3 search tools, 2 MONAI imaging tools, 3 Parabricks genomics tools, 2 multi-NIM pipelines\n- 7 Cortex Agents: Genomics, Chemistry, Structural, Clinical, Workflow, Orchestrator, Discovery\n- 4 Cortex Analyst semantic views: Clinical, Genomics, Compounds, Chemistry\n- Reference data across 5 domains: Genomics (HGNC), ChEMBL (bioactivity), Clinical (ClinicalTrials.gov), Pathways (Reactome, MSigDB, GO), Protein (UniProt)\n- A React web application deployed on Snowflake App Runtime\n\n## Environment Setup\n\n### Clone the Repository\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sf-hcls-solutions.git\ncd sf-hcls-solutions/solutions/scientific-workbench\n```\n\n### Install Snowflake CLI\n\nIf you haven't already, install the Snowflake CLI:\n\n```bash\npip install snowflake-cli\n```\n\n### Configure a Connection\n\nAdd a named connection for deployment. You need ACCOUNTADMIN role for the initial setup:\n\n```bash\nsnow connection add \\\n  --connection-name workbench-deploy \\\n  --account \u003Cyour-account\u003E \\\n  --user \u003Cyour-user\u003E \\\n  --role ACCOUNTADMIN \\\n  --warehouse COMPUTE_WH\n```\n\nTest the connection:\n\n```bash\nsnow connection test --connection workbench-deploy\n```\n\n### Obtain NVIDIA API Key\n\n1. Go to [build.nvidia.com](https://build.nvidia.com) and create an account\n2. Navigate to any BioNeMo NIM (e.g., Boltz-2) and click \"Get API Key\"\n3. Copy the key — you'll provide it during deployment\n\n### Quick Start (One Command)\n\nThe fastest path is the automated deploy script:\n\n```bash\nbash deploy.sh --connection workbench-deploy --nvidia-key \u003Cyour-nvidia-key\u003E\n```\n\nThis runs all 7 phases automatically. If you prefer step-by-step deployment, follow Steps 3 through 7 below. If you used the one-command deploy, skip to Step 8.\n\nFor non-interactive environments (CI, automation):\n\n```bash\nSWB_ASSUME_YES=1 bash deploy.sh --connection workbench-deploy --nvidia-key \u003Cyour-nvidia-key\u003E\n```\n\n### Configuration File (Optional)\n\nFor repeatable deployments or custom naming, create a config file:\n\n```bash\ncp workbench.config.yaml.example workbench.config.yaml\n```\n\nThe config file controls all deployment parameters. Every field is optional — deploy.sh auto-detects defaults if omitted. CLI flags always take precedence over config values.\n\n```yaml\nsnowflake:\n  # Snowflake CLI connection name from ~/.snowflake/config.toml\n  connection: default\n\nsecrets:\n  # NVIDIA API key from build.nvidia.com (for hosted NIM API calls)\n  nvidia_api_key: \"\"\n  # NGC API key from ngc.nvidia.com (for SPCS NIM container weight downloads)\n  ngc_api_key: \"\"\n\ndatabases:\n  workbench: SCIENTIFIC_WORKBENCH      # Platform database\n  reference: WORKBENCH_REFERENCE       # Read-only reference data\n  projects: WORKBENCH_PROJECTS         # Per-project schemas\n\nwarehouses:\n  xs: WORKBENCH_XS                     # Lightweight queries, admin tasks\n  small: WORKBENCH_S                   # Tool execution, data loading\n  ml: WORKBENCH_ML                     # Heavy ML workloads, large data scans\n\n# Enable specific NIM containers on SPCS GPU compute pools\nnim_services:\n  boltz2: false                        # L40S GPU required\n  genmol: false                        # A10G GPU sufficient\n  diffdock: false                      # L40S GPU required\n  rfdiffusion: false                   # L40S GPU required\n  proteinmpnn: false                   # A10G GPU sufficient\n  molmim: false                        # A10G GPU sufficient\n  openfold2: false                     # A10G GPU sufficient\n  openfold3: false                     # L40S GPU required\n  msa_search: false                    # A10G + 500 GiB block volume\n\nagents:\n  deploy_legacy_sql: true              # Deploy agents via SQL scripts\n  deploy_agent_studio: true            # Deploy agents via Agent Studio YAML\n\nmarketplace:\n  pubmed_cke: false                    # Set true if PubMed CKE is subscribed\n  clinicaltrials_cke: false            # Set true if ClinicalTrials.gov CKE is subscribed\n```\n\nDeploy with the config file:\n\n```bash\nbash deploy.sh --config workbench.config.yaml\n```\n\nValidate the config before deploying:\n\n```bash\nbash scripts/validate-config.sh\n```\n\n### deploy.sh Reference\n\nThe deploy script runs 7 phases in sequence. Use flags to skip or isolate phases:\n\n| Flag | Effect |\n|------|--------|\n| `--connection NAME` | Snowflake CLI connection name (auto-detected if omitted) |\n| `--config PATH` | Path to `workbench.config.yaml` |\n| `--nvidia-key KEY` | NVIDIA API key (or `NVIDIA_API_KEY` env var) |\n| `--ngc-key KEY` | NGC API key for SPCS NIMs (or `NGC_API_KEY` env var) |\n| `--skip-prereqs` | Skip Phase 1 EAI/secret creation (if already done) |\n| `--skip-data` | Skip Phase 2 reference data loading (~45 min saved) |\n| `--skip-app` | Skip Phase 7 app deployment |\n| `--skip-tests` | Skip post-deploy verification |\n| `--backend-only` | Deploy only NVIDIA procedures, agents, and app |\n| `--agents-app-only` | Resume at agents + app (skips infrastructure and data) |\n| `--app-only` | Deploy only the SAR app (fastest redeploy) |\n| `--mirror-nims` | Mirror NIM container images from nvcr.io (requires Docker + `--ngc-key`) |\n| `--dry-run` | Print what would be executed without running |\n\n**Deployment phases:**\n\n| Phase | Name | Scripts | Duration |\n|-------|------|---------|----------|\n| 1 | Infrastructure | `00-prerequisites.sql` through `13-execute-tool-by-name.sql` (11 scripts) | 2-5 min |\n| 1b | NIM Mirroring (opt-in) | Docker pull/tag/push of 9 NIM images | 60-120 min |\n| 2 | Reference Data | `data/loaders/*.sql`, `data/synthetic/load_synthetic_data.sql` | 30-45 min |\n| 3 | Tools | `tools/native/*.sql`, `tools/nvidia/*.sql`, `tools/search/*.sql`, `14-custom-tool-onboarding.sql` | 5-10 min |\n| 3.5 | Notebooks | Upload `notebooks/*.ipynb` to workspace | 1-2 min |\n| 4 | Agents | `engine/sql/agents/*.sql`, Agent Studio YAML specs | 3-5 min |\n| 5 | Semantic Views | `data/semantic_views/*.sql`, workflow runner | 2-3 min |\n| 6 | App Deployment | `snow app deploy` (Docker build + SPCS service) | 5-8 min |\n| 7 | Verification | `tests/*.sql`, `CALL RUN_VALIDATION_TESTS()` | 1-2 min |\n\n**Total fresh deployment: ~50-80 minutes** (without NIM mirroring). Subsequent deploys with `--app-only` take 5-8 minutes.\n\n### Teardown\n\nTo remove the entire deployment:\n\n```bash\nbash scripts/teardown.sh --connection workbench-deploy -y\n```\n\n| Flag | Effect |\n|------|--------|\n| `-y` | Skip confirmation prompt |\n| `--dry-run` | Print what would be dropped without executing |\n| `--keep-data` | Drop platform objects but preserve WORKBENCH_REFERENCE data |\n\n## Deploy Infrastructure\n\nThis step creates the Snowflake objects that the workbench runs on: databases, schemas, warehouses, compute pools, RBAC roles, external access integrations, and secrets.\n\n### Phase 1: Run Infrastructure Scripts\n\nIf deploying manually (not using deploy.sh), execute these scripts in order using Snowsight or the Snowflake CLI:\n\n| Order | Script | What It Creates |\n|-------|--------|-----------------|\n| 1 | `scripts/00-prerequisites.sql` | External access integrations (NVIDIA API, PDB, NIM runtime), secrets, network rules |\n| 2 | `scripts/01-databases-and-schemas.sql` | SCIENTIFIC_WORKBENCH (6 schemas), WORKBENCH_REFERENCE (5 schemas), WORKBENCH_PROJECTS |\n| 3 | `scripts/02-warehouses.sql` | WORKBENCH_XS, WORKBENCH_S, WORKBENCH_ML warehouses; NIM_GPU_A10G_POOL, NIM_GPU_L40S_POOL compute pools |\n| 4 | `scripts/03-rbac.sql` | WORKBENCH_ADMIN, WORKBENCH_SCIENTIST, WORKBENCH_VIEWER roles with appropriate grants |\n| 5 | `scripts/05-tool-registry.sql` | CATALOG.TOOLS table, CATALOG.ASSETS table, CATALOG.CHAT_SESSIONS table, REGISTER_TOOL and SEED_ASSETS procedures |\n\n### Database Layout\n\nAfter Phase 1, you have three databases:\n\n```\nSCIENTIFIC_WORKBENCH          -- Platform database\n  CATALOG                     -- Tool registry, assets, agents, chat sessions\n  WORKFLOWS                   -- Workflow templates and run history\n  GOVERNANCE                  -- Promotion log, canary assertions\n  PROVENANCE                  -- Provenance audit trail\n  PROJECTS                    -- Shared project space\n\nWORKBENCH_REFERENCE           -- Read-only reference data\n  GENOMICS                    -- HGNC gene nomenclature\n  CHEMBL                      -- Bioactivity measurements\n  CLINICAL                    -- ClinicalTrials.gov\n  PATHWAYS                    -- Reactome, MSigDB, GO\n  PROTEIN                     -- UniProt entries\n\nWORKBENCH_PROJECTS            -- Per-project schemas\n  DEMO_NSCLC                  -- NSCLC patient cohort demo\n  DEMO_COMPOUNDS              -- Compound screening demo\n  DEMO_CLINICAL               -- Clinical trials demo\n  DEMO_STRUCTURAL             -- Protein targets demo\n```\n\n### RBAC Roles\n\n| Role | Purpose | Typical User |\n|------|---------|-------------|\n| WORKBENCH_ADMIN | Full access: manage tools, approve submissions, configure agents, share data | Platform administrators |\n| WORKBENCH_SCIENTIST | Run tools, execute workflows, view all data, share results | Computational biologists, chemists |\n| WORKBENCH_VIEWER | Read-only access to results and catalog | Managers, reviewers |\n\n### Verify Infrastructure\n\n```sql\n-- Check databases exist\nSHOW DATABASES LIKE 'SCIENTIFIC_WORKBENCH';\nSHOW DATABASES LIKE 'WORKBENCH_REFERENCE';\nSHOW DATABASES LIKE 'WORKBENCH_PROJECTS';\n\n-- Check roles\nSHOW ROLES LIKE 'WORKBENCH_%';\n\n-- Check external access integrations\nSHOW EXTERNAL ACCESS INTEGRATIONS LIKE 'NVIDIA_%';\n```\n\n## Deploy NIMs on SPCS\n\nThis optional step deploys NVIDIA BioNeMo NIM containers locally on Snowpark Container Services (SPCS) GPU compute pools. By default, all NIM tools call the NVIDIA hosted API — SPCS deployment provides an alternative for customers who need air-gapped operation or want to avoid per-call API costs.\n\n### Prerequisites for SPCS NIMs\n\n- Docker installed locally (for image mirroring)\n- NGC API key from [ngc.nvidia.com](https://ngc.nvidia.com) (separate from the build.nvidia.com key)\n- GPU compute pool quotas enabled in your account (A10G and/or L40S)\n\n### GPU Requirements\n\nEach NIM has specific GPU and storage requirements. SPCS nodes have a 93.13 GiB storage cap across all instance families.\n\n| NIM | Container Image | Compressed Size | GPU | Memory | Status |\n|-----|----------------|----------------|-----|--------|--------|\n| Boltz-2 | `nvcr.io/nim/mit/boltz2:1.8.0` | 9.93 GiB | L40S 48 GiB | 32-90 GiB | Verified |\n| GenMol | `nvcr.io/nim/nvidia/genmol:latest` | 9.44 GiB | A10G 24 GiB | 8-24 GiB | Template |\n| DiffDock | `nvcr.io/nim/mit/diffdock:latest` | 15.33 GiB | L40S 48 GiB | 32-64 GiB | Template |\n| RFdiffusion | `nvcr.io/nim/nvidia/rfdiffusion:2.3.0` | 16.37 GiB | L40S 48 GiB | 32-90 GiB | Template |\n| ProteinMPNN | `nvcr.io/nim/nvidia/proteinmpnn:latest` | 8.60 GiB | A10G 24 GiB | 8-24 GiB | Template |\n| MolMIM | `nvcr.io/nim/nvidia/molmim:latest` | 13.04 GiB | A10G 24 GiB | 16-48 GiB | Template |\n| OpenFold2 | `nvcr.io/nim/nvidia/openfold2:latest` | 8-10 GiB | A10G 24 GiB | 16-48 GiB | Template |\n| OpenFold3 | `nvcr.io/nim/nvidia/openfold3:latest` | 10.19 GiB | L40S 48 GiB | 32-90 GiB | Template |\n| MSA-Search | `nvcr.io/nim/nvidia/msa-search:latest` | 8.64 GiB | A10G 24 GiB | 16-48 GiB | Special (needs block volume) |\n| Evo2 | `nvcr.io/nim/nvidia/evo2:latest` | 15.25 GiB | H100/H200 | TBD | Blocked (needs measurement) |\n\n### Compute Pools\n\nThe infrastructure scripts (Step 3) create two GPU compute pools:\n\n```sql\n-- Already created by 02-warehouses.sql:\n-- NIM_GPU_A10G_POOL: GPU_NV_S (A10G 24 GiB) — GenMol, ProteinMPNN, MolMIM, OpenFold2\n-- NIM_GPU_L40S_POOL: GPU_L40S_G1_16 (L40S 48 GiB) — Boltz-2, DiffDock, RFdiffusion, OpenFold3\n\n-- Verify pools exist\nSHOW COMPUTE POOLS LIKE 'NIM_GPU_%';\n```\n\n### Step 1: Mirror NIM Images\n\nNIM containers must be mirrored from NVIDIA's NGC registry (`nvcr.io`) to your Snowflake image repository. This is required because SPCS pulls images from the Snowflake registry, not directly from external registries.\n\n**Automated (via deploy.sh):**\n\n```bash\nbash deploy.sh --connection workbench-deploy \\\n  --nvidia-key \u003Cyour-nvidia-key\u003E \\\n  --ngc-key \u003Cyour-ngc-key\u003E \\\n  --mirror-nims\n```\n\n**Manual (per image):**\n\n```bash\n# Login to NGC registry\ndocker login nvcr.io -u '$oauthtoken' -p \u003Cyour-ngc-key\u003E\n\n# Login to Snowflake image registry\nsnow spcs image-registry login --connection workbench-deploy\n\n# Get your Snowflake registry URL\nREPO_URL=$(snow sql --connection workbench-deploy \\\n  --query \"SHOW IMAGE REPOSITORIES IN SCHEMA SCIENTIFIC_WORKBENCH.CATALOG\" \\\n  --format json | python3 -c \"\nimport json, sys\ndata = json.load(sys.stdin)\nprint([r['repository_url'] for r in data if 'nim_gpu_images' in r.get('repository_url','').lower()][0])\n\")\n\n# Mirror Boltz-2 (example — repeat for each NIM)\ndocker pull nvcr.io/nim/mit/boltz2:1.8.0\ndocker tag nvcr.io/nim/mit/boltz2:1.8.0 $REPO_URL/boltz2:1.8.0\ndocker push $REPO_URL/boltz2:1.8.0\n```\n\n**Important — Apple Silicon users:** Docker on ARM Macs pulls the `arm64` variant by default. SPCS requires `amd64`. Pull by digest or use `--platform linux/amd64`:\n\n```bash\ndocker pull --platform linux/amd64 nvcr.io/nim/mit/boltz2:1.8.0\n```\n\nMirroring all 9 images takes 60-120 minutes depending on bandwidth.\n\n### Step 2: Create SPCS Services\n\nThe service definitions are in `scripts/10-nim-spcs-services.sql`. Only Boltz-2 is uncommented (verified). To deploy it:\n\n```sql\nUSE DATABASE SCIENTIFIC_WORKBENCH;\nUSE SCHEMA CATALOG;\n\nCREATE SERVICE SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC\n  IN COMPUTE POOL NIM_GPU_L40S_POOL\n  FROM SPECIFICATION $$\nspec:\n  containers:\n    - name: boltz2\n      image: /SCIENTIFIC_WORKBENCH/CATALOG/NIM_GPU_IMAGES/boltz2:1.8.0\n      env:\n        NIM_HTTP_API_PORT: \"8000\"\n        NIM_LOG_LEVEL: \"INFO\"\n      secrets:\n        - snowflakeSecret: SCIENTIFIC_WORKBENCH.CATALOG.NGC_API_KEY\n          secretKeyRef: SECRET_STRING\n          envVarName: NGC_API_KEY\n      volumeMounts:\n        - name: dshm\n          mountPath: /dev/shm\n      resources:\n        requests:\n          nvidia.com/gpu: 1\n          memory: 32Gi\n        limits:\n          nvidia.com/gpu: 1\n          memory: 90Gi\n      readinessProbe:\n        port: 8000\n        path: /v1/health/ready\n  endpoints:\n    - name: boltz2\n      port: 8000\n      public: true\n  volumes:\n    - name: dshm\n      source: memory\n      size: 16Gi\n  $$\n  EXTERNAL_ACCESS_INTEGRATIONS = (NIM_RUNTIME_EAI)\n  MIN_INSTANCES = 1\n  MAX_INSTANCES = 1;\n```\n\nKey points about the service spec:\n- **`/dev/shm` volume**: All PyTorch-based NIM containers require a shared memory volume. SPCS uses `source: memory` for this.\n- **`NGC_API_KEY` secret**: Mounted as an environment variable for model weight downloads on first startup.\n- **Readiness probe**: `/v1/health/ready` — the service reports READY only after weights are loaded.\n- **Startup time**: ~7.5 minutes (3.5 min image pull + 4 min weight download). Add 11-15 minutes if the compute pool must provision a new node.\n\n### Step 3: Verify Service Health\n\n```sql\n-- Check service status\nSELECT SYSTEM$GET_SERVICE_STATUS('SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC');\n\n-- View container logs\nSELECT SYSTEM$GET_SERVICE_LOGS('SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC', 0, 'boltz2', 50);\n\n-- Check endpoint URL\nSHOW ENDPOINTS IN SERVICE SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC;\n```\n\n### Endpoint Path Reference\n\nThe hosted API and self-hosted containers use different URL structures. For the hosted API, the base is `https://health.api.nvidia.com`. For self-hosted containers (including SPCS), the base is `http://localhost:8000` (or the SPCS service DNS).\n\n| NIM | Hosted API Path | Self-Hosted Container Path | Docs |\n|-----|----------------|--------------------------|------|\n| **Boltz-2** | `/v1/biology/mit/boltz2/predict` | `/biology/mit/boltz2/predict` | [build.nvidia.com/boltz2](https://build.nvidia.com/mit/boltz2) |\n| **GenMol** | `/v1/biology/nvidia/genmol/generate` | `/biology/nvidia/genmol/generate` | [build.nvidia.com/genmol](https://build.nvidia.com/nvidia/genmol) |\n| **DiffDock** | `/v1/biology/mit/diffdock` | `/biology/mit/diffdock` | [build.nvidia.com/diffdock](https://build.nvidia.com/mit/diffdock) |\n| **RFdiffusion** | `/v1/biology/ipd/rfdiffusion/generate` | `/biology/ipd/rfdiffusion/generate` | [build.nvidia.com/rfdiffusion](https://build.nvidia.com/ipd/rfdiffusion) |\n| **ProteinMPNN** | `/v1/biology/ipd/proteinmpnn/predict` | `/biology/ipd/proteinmpnn/predict` | [build.nvidia.com/proteinmpnn](https://build.nvidia.com/ipd/proteinmpnn) |\n| **MolMIM** | `/v1/biology/nvidia/molmim/generate` | `/biology/nvidia/molmim/generate` | [build.nvidia.com/molmim](https://build.nvidia.com/nvidia/molmim) |\n| **OpenFold2** | `/v1/biology/openfold/openfold2/predict-structure-from-msa-and-template` | `/biology/openfold/openfold2/predict-structure-from-msa-and-template` | [build.nvidia.com/openfold2](https://build.nvidia.com/openfold/openfold2) |\n| **OpenFold3** | `/v1/biology/openfold/openfold3/predict` | `/biology/openfold/openfold3/predict` | [build.nvidia.com/openfold3](https://build.nvidia.com/openfold/openfold3) |\n| **MSA-Search** | `/v1/biology/colabfold/msa-search/predict` | `/biology/colabfold/msa-search/predict` | [build.nvidia.com/msa-search](https://build.nvidia.com/colabfold/msa-search) |\n| **Evo2** | `/v1/biology/arc/evo2-40b/generate` | `/biology/arc/evo2-40b/generate` | [build.nvidia.com/evo2](https://build.nvidia.com/arc/evo2-40b) |\n\nThe pattern: the self-hosted container path is the hosted path minus the `/v1` prefix. The hosted API requires a Bearer token (`Authorization: Bearer \u003CNVIDIA_API_KEY\u003E`); self-hosted containers do not require authentication.\n\nTo verify a container's actual endpoint after deployment, query its OpenAPI spec:\n\n```bash\ncurl http://\u003Cservice-endpoint\u003E:8000/openapi.json | python3 -m json.tool | grep '\"paths\"' -A 20\n```\n\n### Cost Management\n\nSPCS GPU services consume credits continuously while running. To manage costs:\n\n```sql\n-- Suspend a service (stops GPU billing)\nALTER SERVICE SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC SUSPEND;\n\n-- Resume when needed\nALTER SERVICE SCIENTIFIC_WORKBENCH.CATALOG.NIM_BOLTZ2_SVC RESUME;\n```\n\nThe compute pools have auto-suspend configured (300 seconds by default). However, auto-suspend only triggers after all services in the pool are suspended. Suspend services explicitly when not in use.\n\n### Special Cases\n\n- **MSA-Search**: Requires a 500 GiB+ block volume for reference sequence databases (~1.2 TB for full UniRef30/ColabFold). The service spec includes a `block` volume sourced from a stage.\n- **Evo2**: The 40B parameter model's runtime footprint (~89 GiB) is close to the 93.13 GiB node storage cap. Do not deploy until measured on your target instance family. May require H100 80 GiB or H200 141 GiB VRAM.\n\n## Load Reference Data\n\nPhase 2 loads ~40 GB of reference data across 5 scientific domains. This is the longest phase (~30-45 minutes).\n\n### Phase 2: Reference Data\n\n```bash\n# If deploying manually, run these scripts:\n# scripts/04-reference-data.sql (schema setup)\n# data/loaders/load_chembl.sql\n# data/loaders/load_remaining.sql\n# data/synthetic/load_synthetic_data.sql\n```\n\n### Data Domains\n\n| Domain | Schema | Key Tables | Source | Rows (approx) |\n|--------|--------|-----------|--------|---------------|\n| Genomics | GENOMICS | HGNC_GENES | HGNC | 43,000 |\n| Bioactivity | CHEMBL | ACTIVITIES, ASSAYS, MOLECULE_DICTIONARY, TARGET_DICTIONARY | ChEMBL | 2M+ |\n| Clinical | CLINICAL | STUDIES, CONDITIONS, INTERVENTIONS | ClinicalTrials.gov | 500K+ |\n| Pathways | PATHWAYS | PATHWAYS, PATHWAY_GENES, GENE_SETS, GENE_SET_MEMBERS, GO_ANNOTATIONS, GO_TERMS | Reactome, MSigDB, GO | 1M+ |\n| Protein | PROTEIN | UNIPROT_ENTRIES | UniProt | 570K+ |\n\n### Verify Data Loading\n\n```sql\n-- Check row counts across reference schemas\nSELECT TABLE_SCHEMA, TABLE_NAME, ROW_COUNT\nFROM WORKBENCH_REFERENCE.INFORMATION_SCHEMA.TABLES\nWHERE TABLE_SCHEMA IN ('GENOMICS', 'CHEMBL', 'CLINICAL', 'PATHWAYS', 'PROTEIN')\n  AND TABLE_TYPE = 'BASE TABLE'\nORDER BY TABLE_SCHEMA, TABLE_NAME;\n```\n\n## Register Tools\n\nPhase 3 creates the 28 agent-callable tool procedures and registers them in the CATALOG.TOOLS table.\n\n### Tool Taxonomy\n\nThe workbench supports four types of tools:\n\n| Type | Runtime | Example | Count |\n|------|---------|---------|-------|\n| **Native** (Snowpark Python) | Warehouse | `validate_molecule`, `run_differential_expression` | 7 |\n| **NIM Wrapper** (NVIDIA API) | Warehouse + NVIDIA hosted API | `run_boltz2`, `run_genmol`, `run_diffdock` | 11 |\n| **Search** (Cortex Search / CKE) | Cortex Search service | `search_pubmed`, `search_clinical_trials` | 3 |\n| **Pipeline** (multi-tool chain) | Warehouse | `run_drug_discovery_pipeline`, `run_msa_structure_pipeline` | 2 |\n| **Imaging** (MONAI NIM) | Warehouse + NVIDIA hosted API | `run_monai_vista3d`, `run_monai_lung_nodule` | 2 |\n| **Genomics** (Parabricks) | SPCS GPU (pending) | `run_parabricks_fq2bam`, `run_parabricks_deepvariant` | 3 |\n\n### How Tools Are Structured\n\nEvery tool is a Snowflake stored procedure or function with a JSON annotation in its COMMENT field:\n\n```sql\nCREATE OR REPLACE PROCEDURE CATALOG.MY_TOOL(...)\nRETURNS VARCHAR\nLANGUAGE PYTHON\n...\nCOMMENT = 'TOOL:{\"display_name\":\"My Tool\",\"description\":\"...\",\"domains\":\"genomics\",\"params\":{...},\"return_type\":\"VARCHAR\",\"example\":\"CALL CATALOG.MY_TOOL(...)\"}'\nAS $$\n...\n$$;\n```\n\nThe `REGISTER_TOOL` procedure adds the tool to `CATALOG.TOOLS`:\n\n```sql\nCALL CATALOG.REGISTER_TOOL(\n  'my_tool',                                    -- name (unique)\n  'My Tool',                                    -- display_name\n  'Description of what the tool does.',         -- description\n  'genomics,drug-discovery',                    -- domains (comma-separated)\n  'procedure',                                  -- tool_type\n  'SCIENTIFIC_WORKBENCH.CATALOG.MY_TOOL',       -- function_reference\n  '{\"param1\":\"STRING\",\"param2\":\"INT\"}',         -- parameters (JSON)\n  'VARCHAR',                                    -- return_type\n  'CALL CATALOG.MY_TOOL(''value'', 42)'         -- example_usage\n);\n```\n\n### NVIDIA BioNeMo NIM Tools\n\nThese 11 tools call the NVIDIA hosted API via the `NVIDIA_API_EAI` external access integration:\n\n| Tool | NIM Model | What It Does |\n|------|-----------|-------------|\n| `run_boltz2` | Boltz-2 | Protein structure prediction + binding affinity (pIC50) |\n| `run_genmol` | GenMol | De novo drug-like molecule generation |\n| `run_diffdock` | DiffDock | Small-molecule docking (binding pose prediction) |\n| `run_proteinmpnn` | ProteinMPNN | Inverse folding (backbone to sequence design) |\n| `run_rfdiffusion` | RFdiffusion | De novo protein backbone generation |\n| `run_molmim` | MolMIM | Latent-space molecule optimization |\n| `run_openfold2` | OpenFold2 | Single-chain protein structure prediction |\n| `run_openfold3` | OpenFold3 | Multi-chain complex structure prediction |\n| `run_msa_search` | MSA-Search | Multiple sequence alignment via ColabFold |\n| `run_evo2` | Evo2 | DNA/RNA sequence generation (40B parameter model) |\n| `run_drug_discovery_pipeline` | GenMol + DiffDock | End-to-end: generate, validate, dock, rank |\n\n### Verify Tool Registration\n\n```sql\nSELECT tool_type, COUNT(*) AS count\nFROM SCIENTIFIC_WORKBENCH.CATALOG.TOOLS\nWHERE status = 'active'\nGROUP BY tool_type\nORDER BY count DESC;\n\n-- Expected: 28 total active tools\nSELECT COUNT(*) AS total_active_tools\nFROM SCIENTIFIC_WORKBENCH.CATALOG.TOOLS\nWHERE status = 'active';\n```\n\n## Configure AI Services\n\nPhase 4-6 sets up Cortex Search services, the governance framework, workflow engine, semantic views, and the agent layer.\n\n### Cortex Search and Governance\n\n| Script | What It Creates |\n|--------|-----------------|\n| `scripts/06-cortex-services.sql` | Cortex Search services for tool discovery and asset catalog |\n| `scripts/07-governance.sql` | GOVERNANCE.PROMOTION_LOG, GOVERNANCE.CANARY_ASSERTIONS |\n| `scripts/08-workflow-engine.sql` | WORKFLOWS.TEMPLATES, WORKFLOWS.RUNS, workflow execution procedures |\n\n### Semantic Views (Cortex Analyst)\n\nFour semantic views enable natural-language queries over structured data:\n\n| Semantic View | Underlying Data | Example Query |\n|--------------|----------------|---------------|\n| `SV_CLINICAL` | DEMO_NSCLC patient cohort | \"How many patients are in stage IIIA?\" |\n| `SV_GENOMICS` | Gene expression + pathways | \"Which genes are upregulated in the NSCLC cohort?\" |\n| `SV_COMPOUNDS` | Compound IC50 assay results | \"Show me compounds with IC50 below 100 nM against EGFR\" |\n| `SV_CHEMISTRY` | ChEMBL molecule reference | \"List all approved kinase inhibitors\" |\n\n### Agent Architecture\n\nThe workbench uses a multi-agent architecture with specialized domain agents coordinated by an orchestrator:\n\n```\n                    DISCOVERY_AGENT\n                    (user-facing)\n                         |\n               uses semantic views +\n               search + code execution\n                         |\n                  ORCHESTRATOR_AGENT\n                  (routes to domains)\n                   /     |     \\\n        GENOMICS   CHEMISTRY  STRUCTURAL\n         AGENT      AGENT      AGENT\n           |          |          |\n        5 tools    4 tools    9 tools\n                         |\n                   CLINICAL_AGENT\n                   (4 semantic views)\n```\n\nEach domain agent has access only to its relevant tools. The ORCHESTRATOR_AGENT routes queries to the correct domain and synthesizes cross-domain results. The DISCOVERY_AGENT is the user-facing agent that also has direct access to semantic views and code execution.\n\n### Verify Agent Configuration\n\n```sql\n-- Test the Discovery Agent with a simple query\nSELECT SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n  'SCIENTIFIC_WORKBENCH.CATALOG.DISCOVERY_AGENT',\n  '{\"query\": \"What tools are available for protein structure prediction?\"}',\n  TRUE\n) AS response;\n```\n\n## Deploy the Application\n\nPhase 7 deploys the React web application to Snowflake App Runtime (SAR).\n\n### SAR Deployment\n\nFrom the `app/` directory:\n\n```bash\ncd solutions/scientific-workbench/app\nsnow app deploy --connection workbench-deploy\n```\n\nThis performs three steps:\n1. Uploads source files to a Snowflake workspace\n2. Builds a Docker container in the cloud (via SPCS build service)\n3. Creates or upgrades the APPLICATION SERVICE\n\nBuild time is approximately 3-5 minutes. The output includes the app URL:\n\n```\nApp ready at https://\u003Chash\u003E-\u003Caccount\u003E.snowflakecomputing.app\n```\n\n### Grant Access to Users\n\nAfter deployment, grant access to the appropriate roles:\n\n```sql\n-- Scientists can use the app\nGRANT USAGE ON APPLICATION SERVICE SCIENTIFIC_WORKBENCH_APP\n  TO ROLE WORKBENCH_SCIENTIST;\n\n-- Viewers get read-only access\nGRANT USAGE ON APPLICATION SERVICE SCIENTIFIC_WORKBENCH_APP\n  TO ROLE WORKBENCH_VIEWER;\n```\n\n### App Modules\n\nThe application includes 10 modules:\n\n| Module | Path | Description |\n|--------|------|-------------|\n| Home | `/` | Dashboard with tool count, agent status, recent activity |\n| Chat | `/chat` | Conversational interface to the Discovery Agent |\n| Explore | `/explore` | Schema browser with column profiling and data preview |\n| Experiments | `/experiments` | Workflow run history and results viewer |\n| Asset Catalog | `/catalog` | Searchable catalog of all platform assets with data preview |\n| Tools | `/tools` | Registry of all 28 agent-callable tools |\n| Tool Onboard | `/tools/onboard` | Custom tool submission wizard |\n| Governance | `/governance` | Promotion log, canary assertions, provenance trail |\n| Notebooks | `/notebooks` | Snowflake notebook launcher |\n| Share | `/share` | Cross-account data sharing (admin/scientist) |\n\n### Verify Deployment\n\nOpen the app URL in your browser. You should see:\n- Home page with \"28 Active Tools\" (or similar count)\n- Chat page where you can talk to the Discovery Agent\n- Tools page listing all registered tools\n\n## Local Development\n\nFor development and testing, you can run the app locally against your Snowflake account.\n\n### Setup\n\n```bash\ncd solutions/scientific-workbench/app\nnpm install\n```\n\n### Environment Variables\n\nCreate a `.env.local` file (or rely on `~/.snowflake/config.toml` default connection):\n\n```bash\n# Option 1: Key-pair auth (recommended)\nSNOWFLAKE_ACCOUNT=\u003Caccount\u003E\nSNOWFLAKE_USER=\u003Cuser\u003E\nSNOWFLAKE_PRIVATE_KEY_PATH=~/.snowflake/rsa_key.p8\nSNOWFLAKE_WAREHOUSE=WORKBENCH_XS\nSNOWFLAKE_ROLE=WORKBENCH_ADMIN\n\n# Option 2: config.toml (zero config — uses default connection)\n# No env vars needed if ~/.snowflake/config.toml is configured\n\n# Enable admin features in local dev\nSWB_LOCAL_DEV_ADMIN=true\n```\n\n### Run the Dev Server\n\n```bash\nnpm run dev\n```\n\nThe app starts at `http://localhost:3000`. Hot reload is enabled — changes to pages and API routes take effect immediately.\n\n### Key Differences from SPCS\n\n| Aspect | Local Dev | SPCS (Deployed) |\n|--------|-----------|-----------------|\n| Auth | Password or config.toml | SPCS service token |\n| Caller's rights | Not available (use SWB_LOCAL_DEV_ADMIN) | Reads sf-context-current-user-token header |\n| URL | localhost:3000 | `\u003Chash\u003E-\u003Caccount\u003E.snowflakecomputing.app` |\n| Secrets | Environment variables | `getSecret()` API from app.yml |\n\n### Testing Changes\n\nBefore deploying, verify your changes build successfully:\n\n```bash\nnpx tsc --noEmit       # Type checking\nnpx next build         # Full production build\n```\n\nThen deploy with `snow app deploy --connection workbench-deploy`.\n\n## Run a Discovery Workflow\n\nNow that the workbench is deployed, walk through an end-to-end drug discovery workflow.\n\n### Open the Chat\n\nNavigate to the Chat page in the web application. You'll see the Discovery Agent interface.\n\n### Example: Multi-Step Drug Discovery\n\nTry this prompt:\n\n\u003E Find compounds in ChEMBL that target EGFR with IC50 below 100 nM, validate their drug-likeness, and predict binding poses for the top 3 candidates against PDB structure 1M17.\n\nThe agent will:\n\n1. Query the `SV_COMPOUNDS` semantic view to find EGFR compounds with IC50 \u003C 100 nM\n2. Call `validate_molecule` on each candidate (RDKit sanitization + PAINS/BRENK filters)\n3. Call `run_diffdock` to dock the top 3 validated molecules against PDB 1M17\n4. Return structured results with confidence scores and binding poses\n\n### Example: Genomics Analysis\n\n\u003E Run differential expression analysis comparing Stage I vs Stage III patients in the NSCLC cohort, then perform pathway enrichment on the upregulated genes.\n\nThe agent will:\n\n1. Call `run_differential_expression` on the NSCLC gene expression data\n2. Filter for significantly upregulated genes (log2FC \u003E 1, FDR \u003C 0.05)\n3. Call `run_pathway_enrichment` against MSigDB Hallmark gene sets\n4. Return enriched pathways with Fisher exact test p-values\n\n### Example: Protein Structure Prediction\n\n\u003E Predict the structure of the first 200 residues of human TP53 protein and assess confidence.\n\nThe agent will:\n\n1. Look up the TP53 sequence (or ask you to provide it)\n2. Call `run_openfold2` for single-chain structure prediction\n3. Return the predicted structure with pLDDT confidence scores\n\n### View Results\n\nAfter each workflow, results are saved to output tables. View them in:\n- **Experiments** page — workflow run history with parameters and status\n- **Explore** page — browse the output table schema and preview rows\n- **Asset Catalog** — newly created result tables appear as assets\n\n## Extend the Platform\n\n### Adding a New Native Tool\n\nCreate a Snowpark Python procedure in `SCIENTIFIC_WORKBENCH.CATALOG`:\n\n```sql\nCREATE OR REPLACE PROCEDURE CATALOG.MY_NEW_TOOL(\n    \"P_INPUT\" VARCHAR,\n    \"P_OUTPUT_TABLE\" VARCHAR\n)\nRETURNS VARCHAR\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.11'\nPACKAGES = ('snowflake-snowpark-python')\nHANDLER = 'run'\nCOMMENT = 'TOOL:{\"display_name\":\"My New Tool\",\"description\":\"What it does\",\"domains\":\"genomics\",\"params\":{\"input\":\"STRING\",\"output_table\":\"STRING\"},\"return_type\":\"VARCHAR\",\"example\":\"CALL CATALOG.MY_NEW_TOOL(input, output)\"}'\nAS $$\nimport json\n\ndef run(session, p_input: str, p_output_table: str) -\u003E str:\n    # Your tool logic here\n    result = {\"status\": \"success\", \"output_table\": p_output_table}\n    return json.dumps(result)\n$$;\n```\n\nRegister it:\n\n```sql\nCALL CATALOG.REGISTER_TOOL(\n    'my_new_tool', 'My New Tool',\n    'What it does',\n    'genomics', 'procedure',\n    'SCIENTIFIC_WORKBENCH.CATALOG.MY_NEW_TOOL',\n    '{\"input\":\"STRING\",\"output_table\":\"STRING\"}',\n    'VARCHAR',\n    'CALL CATALOG.MY_NEW_TOOL(''test'', ''RESULTS.OUTPUT'')'\n);\n```\n\nRefresh the asset catalog:\n\n```sql\nCALL CATALOG.SEED_ASSETS();\n```\n\n### Adding a New NIM Wrapper\n\nFollow the same pattern as existing NIM tools. Key requirements:\n\n1. Add `EXTERNAL_ACCESS_INTEGRATIONS = (NVIDIA_API_EAI)` to the procedure\n2. Add `SECRETS = ('nvidia_key' = SCIENTIFIC_WORKBENCH.CATALOG.NVIDIA_API_SECRET)`\n3. Use the `_snowflake.get_generic_secret_string('nvidia_key')` API to read the key\n4. Call the NIM endpoint via `requests.post()` with Bearer token auth\n\n### Custom Tool Onboarding\n\nScientists can submit custom tools through the web application:\n\n1. Navigate to **Tools \u003E Onboard** in the app\n2. Fill in the tool specification (name, description, parameters, code)\n3. Submit for review\n4. An admin reviews and approves/rejects via **Tools \u003E Submissions**\n5. Approved tools are automatically registered in the catalog\n\n### Adding Reference Data\n\nTo add a new reference dataset:\n\n1. Create a schema in WORKBENCH_REFERENCE (or use an existing one)\n2. Load the data\n3. Register it as an asset:\n\n```sql\nINSERT INTO SCIENTIFIC_WORKBENCH.CATALOG.ASSETS\n    (asset_id, asset_name, asset_type, description, domain, schema_name, owner)\nVALUES\n    ('asset-data-WORKBENCH_REFERENCE.MY_SCHEMA.MY_TABLE',\n     'MY_TABLE', 'dataset', 'Description of the dataset',\n     'MY_SCHEMA', 'MY_SCHEMA', CURRENT_USER());\n```\n\nOr run `CALL CATALOG.SEED_ASSETS()` to auto-discover tables in registered schemas.\n\n## Security Model\n\n### Platform Roles and Hierarchy\n\nThe workbench uses three Snowflake database roles in a hierarchical inheritance chain:\n\n```\nSYSADMIN\n  └── WORKBENCH_ADMIN       (full platform administration)\n        └── WORKBENCH_SCIENTIST   (run tools, execute workflows, share results)\n              └── WORKBENCH_VIEWER      (read-only access to results and catalog)\n```\n\nEach higher role inherits all privileges of the roles below it. WORKBENCH_ADMIN inherits SCIENTIST, which inherits VIEWER.\n\n| Role | Can Do | Cannot Do |\n|------|--------|-----------|\n| **WORKBENCH_ADMIN** | Everything: manage tools, approve custom tool submissions, configure agents, share data cross-account, manage SPCS services, run governance procedures | N/A (full access) |\n| **WORKBENCH_SCIENTIST** | Run all tools, execute workflows, view all data, create result tables, share results, write to provenance log, submit custom tools | Approve/reject tool submissions, manage compute pools, run governance procedures |\n| **WORKBENCH_VIEWER** | Read catalog, view results, view tool registry, use XS warehouse | Run tools, execute workflows, create tables, share data |\n\n### Access Control by Schema\n\n| Database.Schema | ADMIN | SCIENTIST | VIEWER |\n|----------------|-------|-----------|--------|\n| SCIENTIFIC_WORKBENCH.CATALOG | Full DDL | SELECT + CALL procedures | SELECT only |\n| SCIENTIFIC_WORKBENCH.RESULTS | Full DDL | SELECT + INSERT + CREATE TABLE | SELECT only |\n| SCIENTIFIC_WORKBENCH.WORKFLOWS | Full DDL | SELECT + CALL procedures | No access |\n| SCIENTIFIC_WORKBENCH.GOVERNANCE | Full DDL + procedures | SELECT only | No access |\n| SCIENTIFIC_WORKBENCH.PROVENANCE | Full DDL | SELECT + INSERT | No access |\n| WORKBENCH_REFERENCE.* | Full DDL | SELECT (read-only) | No access |\n| WORKBENCH_PROJECTS.* | Full DDL | SELECT (read-only) | No access |\n\n### Scientific Personas\n\nIn a typical deployment, the platform roles map to scientific personas. Assign users to the appropriate role based on their function:\n\n| Persona | Snowflake Role | What They Do |\n|---------|---------------|-------------|\n| **Computational Biologist** | WORKBENCH_SCIENTIST | Differential expression analysis, pathway enrichment, survival analysis, gene symbol validation |\n| **Medicinal Chemist** | WORKBENCH_SCIENTIST | Molecule generation (GenMol), optimization (MolMIM), validation (RDKit + PAINS), molecular descriptors |\n| **Structural Biologist** | WORKBENCH_SCIENTIST | Protein structure prediction (Boltz-2, OpenFold2/3), backbone design (RFdiffusion), sequence design (ProteinMPNN), molecular docking (DiffDock) |\n| **Clinical Data Scientist** | WORKBENCH_SCIENTIST | Cohort queries via semantic views, clinical trial search, patient outcome analysis |\n| **AI Drug Discovery Scientist** | WORKBENCH_SCIENTIST | End-to-end pipelines, multi-tool workflows, agent-driven discovery |\n| **Platform Administrator** | WORKBENCH_ADMIN | Tool onboarding, agent configuration, data sharing, governance, compute management |\n| **Manager / Reviewer** | WORKBENCH_VIEWER | Review experiment results, browse catalog, audit provenance |\n\n### Assigning Roles to Users\n\n```sql\n-- Grant a scientist role to a user\nGRANT ROLE WORKBENCH_SCIENTIST TO USER jane_doe;\n\n-- Grant admin role\nGRANT ROLE WORKBENCH_ADMIN TO USER platform_admin;\n\n-- Grant viewer role\nGRANT ROLE WORKBENCH_VIEWER TO USER manager_smith;\n```\n\nUsers switch to their workbench role in a Snowflake session:\n\n```sql\nUSE ROLE WORKBENCH_SCIENTIST;\n```\n\nIn the web application, caller's rights automatically detects the user's active role via the SPCS user token — no manual `USE ROLE` needed.\n\n### Named Query Allowlist\n\nThe web application does not execute arbitrary SQL from the client. All queries go through a **named query registry** (`lib/named-queries.ts`) that maps query keys to pre-defined SQL templates:\n\n- **Static queries** (no parameters): SQL is a compile-time constant — zero attack surface\n- **Parameterized queries**: Parameters are validated with Zod schemas and used as bind variables (`?`) — no string interpolation\n\n```\nClient: { query: \"column_info\", params: { schema: \"GENOMICS\", table: \"HGNC_GENES\" } }\n                                    |\n                              resolveNamedQuery()\n                                    |\nServer: SELECT COLUMN_NAME, DATA_TYPE FROM INFORMATION_SCHEMA.COLUMNS\n        WHERE TABLE_SCHEMA = ? AND TABLE_NAME = ?\n        binds: ['GENOMICS', 'HGNC_GENES']\n```\n\n### Authorization Patterns\n\nAPI routes use caller's rights to check the user's Snowflake role:\n\n```typescript\n// Check caller's role via SPCS user token\nconst [row] = await querySnowflake(\"SELECT CURRENT_ROLE() AS role\", { callersRights: true })\nconst callerRole = String(row?.ROLE ?? \"\").toUpperCase()\n\nif (!ALLOWED_ROLES.has(callerRole)) {\n  return Response.json({ error: \"Forbidden\" }, { status: 403 })\n}\n```\n\n| Endpoint | Required Role |\n|----------|--------------|\n| GET /api/tools/submissions | Any authenticated user |\n| POST /api/tools/submissions (approve/reject) | WORKBENCH_ADMIN |\n| POST /api/share (grant/create) | WORKBENCH_ADMIN or WORKBENCH_SCIENTIST |\n| GET /api/chat/sessions | Scoped to CURRENT_USER() |\n\n### External Access Integrations\n\n| EAI | Purpose | Allowed Hosts |\n|-----|---------|---------------|\n| NVIDIA_API_EAI | NVIDIA BioNeMo NIM API calls | `integrate.api.nvidia.com`, `api.nvcf.nvidia.com` |\n| PDB_API_EAI | RCSB Protein Data Bank lookups | `data.rcsb.org` |\n| NIM_RUNTIME_EAI | SPCS NIM container weight downloads | `*.nvidia.com`, `*.nvcr.io` |\n\n## Conclusion and Resources\n\nCongratulations! You've deployed a complete Scientific Workbench for life sciences R&D on Snowflake. The platform provides:\n\n- A multi-agent AI system with 28 tools spanning genomics, chemistry, structural biology, and clinical data\n- NVIDIA BioNeMo NIM integration for state-of-the-art molecular and protein AI\n- A governed, role-based environment with audit trails and provenance tracking\n- A modern web application for interactive discovery workflows\n\n### What You Learned\n\n- Deploying a multi-database, multi-schema Snowflake platform with automated scripts\n- Configuring NVIDIA BioNeMo NIMs as agent-callable tools\n- Setting up Cortex Agents with domain-specific tool routing\n- Building and deploying a Snowflake App Runtime (Next.js) application\n- Extending the platform with new tools, data, and semantic views\n- Implementing security patterns: named query allowlists, RBAC, caller's rights\n\n### Related Resources\n\n- [Snowflake Cortex Agents Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n- [Snowflake App Runtime Documentation](https://docs.snowflake.com/en/developer-guide/snowflake-app-runtime/about-snowflake-app-runtime)\n- [NVIDIA BioNeMo Platform](https://www.nvidia.com/en-us/clara/bionemo/)\n- [NVIDIA NIM API Catalog](https://build.nvidia.com/explore/healthcare)\n- [Cortex Analyst (Semantic Views)](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)\n- [Source Repository](https://github.com/Snowflake-Labs/sf-hcls-solutions)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-18fe192efd","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-dc139cd470",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-1285b1e51c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-09",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-e4976efbc7","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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