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streamlit/part7/solubility-app.py

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+######################
+# Import libraries
+######################
+import numpy as np
+import pandas as pd
+import streamlit as st
+import pickle
+from PIL import Image
+from rdkit import Chem
+from rdkit.Chem import Descriptors
+
+######################
+# Custom function
+######################
+## Calculate molecular descriptors
+def AromaticProportion(m):
+  aromatic_atoms = [m.GetAtomWithIdx(i).GetIsAromatic() for i in range(m.GetNumAtoms())]
+  aa_count = []
+  for i in aromatic_atoms:
+    if i==True:
+      aa_count.append(1)
+  AromaticAtom = sum(aa_count)
+  HeavyAtom = Descriptors.HeavyAtomCount(m)
+  AR = AromaticAtom/HeavyAtom
+  return AR
+
+def generate(smiles, verbose=False):
+
+    moldata= []
+    for elem in smiles:
+        mol=Chem.MolFromSmiles(elem)
+        moldata.append(mol)
+
+    baseData= np.arange(1,1)
+    i=0
+    for mol in moldata:
+
+        desc_MolLogP = Descriptors.MolLogP(mol)
+        desc_MolWt = Descriptors.MolWt(mol)
+        desc_NumRotatableBonds = Descriptors.NumRotatableBonds(mol)
+        desc_AromaticProportion = AromaticProportion(mol)
+
+        row = np.array([desc_MolLogP,
+                        desc_MolWt,
+                        desc_NumRotatableBonds,
+                        desc_AromaticProportion])
+
+        if(i==0):
+            baseData=row
+        else:
+            baseData=np.vstack([baseData, row])
+        i=i+1
+
+    columnNames=["MolLogP","MolWt","NumRotatableBonds","AromaticProportion"]
+    descriptors = pd.DataFrame(data=baseData,columns=columnNames)
+
+    return descriptors
+
+######################
+# Page Title
+######################
+
+image = Image.open('solubility-logo.jpg')
+
+st.image(image, use_column_width=True)
+
+st.write("""
+# Molecular Solubility Prediction Web App
+
+This app predicts the **Solubility (LogS)** values of molecules!
+
+Data obtained from the John S. Delaney. [ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005.
+***
+""")
+
+
+######################
+# Input molecules (Side Panel)
+######################
+
+st.sidebar.header('User Input Features')
+
+## Read SMILES input
+SMILES_input = "NCCCC\nCCC\nCN"
+
+SMILES = st.sidebar.text_area("SMILES input", SMILES_input)
+SMILES = "C\n" + SMILES #Adds C as a dummy, first item
+SMILES = SMILES.split('\n')
+
+st.header('Input SMILES')
+SMILES[1:] # Skips the dummy first item
+
+## Calculate molecular descriptors
+st.header('Computed molecular descriptors')
+X = generate(SMILES)
+X[1:] # Skips the dummy first item
+
+######################
+# Pre-built model
+######################
+
+# Reads in saved model
+load_model = pickle.load(open('solubility_model.pkl', 'rb'))
+
+# Apply model to make predictions
+prediction = load_model.predict(X)
+#prediction_proba = load_model.predict_proba(X)
+
+st.header('Predicted LogS values')
+prediction[1:] # Skips the dummy first item