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Build a Bioinformatics Solubility Dashboard
import streamlit as st
st.title('☘️ Solubility Dashboard')
# Data Filtering
mol_size = st.slider('Select a value', 100, 500, 300)
df['MOLWT_CLASS'] = pd.Series(['small' if x < mol_size else 'large' for x in df['MOLWT']])
df_class = df.groupby('MOLWT_CLASS').mean().reset_index()
st.divider()
# Calculate Metrics
molwt_large = round(df_class['MOLWT'][0], 2)
molwt_small = round(df_class['MOLWT'][1], 2)
numrotatablebonds_large = round(df_class['NUMROTATABLEBONDS'][0], 2)
numrotatablebonds_small = round(df_class['NUMROTATABLEBONDS'][1], 2)
import streamlit as st
st.title('☘️ Solubility Dashboard')
# Data Filtering
mol_size = st.slider('Select a value', 100, 500, 300)
df['MOLWT_CLASS'] = pd.Series(['small' if x < mol_size else 'large' for x in df['MOLWT']])
df_class = df.groupby('MOLWT_CLASS').mean().reset_index()
st.divider()
# Calculate Metrics
molwt_large = round(df_class['MOLWT'][0], 2)
molwt_small = round(df_class['MOLWT'][1], 2)
numrotatablebonds_large = round(df_class['NUMROTATABLEBONDS'][0], 2)
numrotatablebonds_small = round(df_class['NUMROTATABLEBONDS'][1], 2)
import streamlit as st
st.title('☘️ Solubility Dashboard')
# Data Filtering
mol_size = st.slider('Select a value', 100, 500, 300)
df['MOLWT_CLASS'] = pd.Series(['small' if x < mol_size else 'large' for x in df['MOLWT']])
df_class = df.groupby('MOLWT_CLASS').mean().reset_index()
st.divider()
# Calculate Metrics
molwt_large = round(df_class['MOLWT'][0], 2)
molwt_small = round(df_class['MOLWT'][1], 2)
numrotatablebonds_large = round(df_class['NUMROTATABLEBONDS'][0], 2)
numrotatablebonds_small = round(df_class['NUMROTATABLEBONDS'][1], 2)
import streamlit as st
st.title('☘️ Solubility Dashboard')
# Data Filtering
mol_size = st.slider('Select a value', 100, 500, 300)
df['MOLWT_CLASS'] = pd.Series(['small' if x < mol_size else 'large' for x in df['MOLWT']])
df_class = df.groupby('MOLWT_CLASS').mean().reset_index()
st.divider()
# Calculate Metrics
molwt_large = round(df_class['MOLWT'][0], 2)
molwt_small = round(df_class['MOLWT'][1], 2)
numrotatablebonds_large = round(df_class['NUMROTATABLEBONDS'][0], 2)
numrotatablebonds_small = round(df_class['NUMROTATABLEBONDS'][1], 2)
import streamlit as st
st.title('☘️ Solubility Dashboard')
# Data Filtering
mol_size = st.slider('Select a value', 100, 500, 300)
df['MOLWT_CLASS'] = pd.Series(['small' if x < mol_size else 'large' for x in df['MOLWT']])
df_class = df.groupby('MOLWT_CLASS').mean().reset_index()
st.divider()
# Calculate Metrics
molwt_large = round(df_class['MOLWT'][0], 2)
molwt_small = round(df_class['MOLWT'][1], 2)
numrotatablebonds_large = round(df_class['NUMROTATABLEBONDS'][0], 2)
numrotatablebonds_small = round(df_class['NUMROTATABLEBONDS'][1], 2)
import streamlit as st
st.title('☘️ Solubility Dashboard')
# Data Filtering
mol_size = st.slider('Select a value', 100, 500, 300)
df['MOLWT_CLASS'] = pd.Series(['small' if x < mol_size else 'large' for x in df['MOLWT']])
df_class = df.groupby('MOLWT_CLASS').mean().reset_index()
st.divider()
# Calculate Metrics
molwt_large = round(df_class['MOLWT'][0], 2)
molwt_small = round(df_class['MOLWT'][1], 2)
numrotatablebonds_large = round(df_class['NUMROTATABLEBONDS'][0], 2)
numrotatablebonds_small = round(df_class['NUMROTATABLEBONDS'][1], 2)
import streamlit as st
st.title('☘️ Solubility Dashboard')
# Data Filtering
mol_size = st.slider('Select a value', 100, 500, 300)
df['MOLWT_CLASS'] = pd.Series(['small' if x < mol_size else 'large' for x in df['MOLWT']])
df_class = df.groupby('MOLWT_CLASS').mean().reset_index()
st.divider()
# Calculate Metrics
molwt_large = round(df_class['MOLWT'][0], 2)
molwt_small = round(df_class['MOLWT'][1], 2)
numrotatablebonds_large = round(df_class['NUMROTATABLEBONDS'][0], 2)
numrotatablebonds_small = round(df_class['NUMROTATABLEBONDS'][1], 2)
Overview
Using Snowflake, Python, and Streamlit, you can create an interactive tool that helps visualize and understand the relationships between molecular weight, rotatable bonds, LogP values, and aromatic proportions - all key factors in determining a molecule's solubility and potential as a drug candidate.
Build an interactive dashboard that visualizes important molecular properties and their relationship to solubility, providing valuable insights for pharmaceutical research.
Overview of this bioinformatics project

An interactive bioinformatics dashboard that visualizes molecular properties from the Delaney solubility dataset, allowing researchers to explore relationships between molecular weight, rotatable bonds, LogP values, and aromatic proportions.
This solution was created by an in-house Snowflake expert and has been verified to work with current Snowflake instances as of the date of publication.
Solution not working as expected? Contact our team for assistance.