Add working demo version 0

.streamlit/config.toml

@@ -0,0 +1,2 @@
+[theme]
+base="light"

app.py

@@ -0,0 +1,166 @@
+import os
+import tempfile
+from io import StringIO
+
+import joblib
+import numpy as np
+import pandas as pd
+import streamlit as st
+from b3clf.descriptor_padel import compute_descriptors
+from b3clf.geometry_opt import geometry_optimize
+from b3clf.utils import (get_descriptors, predict_permeability,
+                         scale_descriptors, select_descriptors)
+from streamlit_ketcher import st_ketcher
+
+# from geometry_opt import geometry_optimize
+
+# Load the pre-trained model and feature scaler
+model = joblib.load(
+    "/home/legend/Desktop/work/BBB_database/bbb_imbalanced_github/B3clf/b3clf/pre_trained/b3clf_knn_kmeans_SMOTE.joblib"
+)
+scaler = joblib.load(
+    "/home/legend/Desktop/work/BBB_database/bbb_imbalanced_github/B3clf/b3clf/pre_trained/b3clf_scaler.joblib"
+)
+
+
+# Define a function to generate predictions
+# def generate_predictions(file):
+#     # Read the input file
+#     if file.type == "text/csv":
+#         df = pd.read_csv(file)
+#     elif file.type == "chemical/x-mdl-sdfile":
+#         df = pd.read_sdf(file)
+#     else:
+#         st.error("Invalid file type. Please upload a CSV or SDF file.")
+#         return
+
+#     # Compute the molecular geometry, calculate the features, and perform the predictions
+#     X = df.drop("ID", axis=1)
+#     X_scaled = scaler.transform(X)
+#     y_pred_proba = model.predict_proba(X_scaled)[:, 1]
+#     y_pred = model.predict(X_scaled)
+
+#     # Create a DataFrame with the predictions
+#     results = pd.DataFrame({"ID": df["ID"], "B3clf_predicted_probability": y_pred_proba, "B3clf_predicted_label": y_pred})
+
+#     return results
+
+keep_features = "no"
+keep_sdf = "no"
+
+
+def generate_predictions(
+    uploaded_file: st.file_uploader,
+    sep: str = "\s+|\t+",
+    clf: str = "xgb",
+    sampling: str = "classic_ADASYN",
+    time_per_mol: int = 120,
+):
+    """
+    Generate predictions for a given input file.
+    """
+    # mol_tag = os.path.splitext(uploaded_file.name)[0]
+    # uploaded_file = uploaded_file.read().decode("utf-8")
+    mol_tag = os.path.basename(uploaded_file).split(".")[0]
+    internal_sdf = f"{mol_tag}_optimized_3d.sdf"
+
+    # Geometry optimization
+    # Input:
+    # * Either an SDF file with molecular geometries or a text file with SMILES strings
+
+    geometry_optimize(input_fname=uploaded_file, output_sdf=internal_sdf, sep=sep)
+
+    df_features = compute_descriptors(
+        sdf_file=internal_sdf,
+        excel_out=None,
+        output_csv=None,
+        timeout=None,
+        time_per_molecule=time_per_mol,
+    )
+    # st.write(df_features)
+
+    # Get computed descriptors
+    X_features, info_df = get_descriptors(df=df_features)
+
+
+    # Select descriptors
+    X_features = select_descriptors(df=X_features)
+
+    # Scale descriptors
+    X_features = scale_descriptors(df=X_features)
+
+    # Get classifier
+    # clf = get_clf(clf_str=clf, sampling_str=sampling)
+
+    # Get classifier
+    result_df = predict_permeability(
+        clf_str=clf,
+        sampling_str=sampling,
+        features_df=X_features,
+        info_df=info_df,
+        threshold="none",
+    )
+
+    # Get classifier
+    display_cols = [
+        "ID",
+        "SMILES",
+        "B3clf_predicted_probability",
+        "B3clf_predicted_label",
+    ]
+
+    result_df = result_df[
+        [col for col in result_df.columns.to_list() if col in display_cols]
+    ]
+
+    os.remove(internal_sdf)
+
+    return X_features, result_df
+
+
+# Create the Streamlit app
+st.title("BBB Permeability Prediction with Imbalanced Learning")
+
+# Create a file uploader
+
+st.subheader("Input Data")
+file = st.file_uploader("Upload a CSV or SDF file",
+                        type=["csv", "sdf", "txt"],
+                        # accept_multiple_files=False,
+                        )
+# st.write("The content of the file will be displayed below once uploaded.")
+if file:
+    # if "csv" in file.name or "txt" in file.name:
+    #     st.write(file.read().decode("utf-8"))
+    st.write(file)
+
+
+# Generate predictions when the user uploads a file
+if file:
+    temp_dir = tempfile.mkdtemp()
+    # Create a temporary file path for the uploaded file
+    temp_file_path = os.path.join(temp_dir, file.name)
+    # Save the uploaded file to the temporary file path
+    with open(temp_file_path, 'wb') as temp_file:
+        temp_file.write(file.read())
+    X_features, results = generate_predictions(temp_file_path)
+
+    feature_column, prediction_column = st.columns(2)
+
+    # feture table
+    with feature_column:
+        st.subheader("Features")
+        st.dataframe(X_features)
+
+    # prediction table
+    with prediction_column:
+        st.subheader("Predictions")
+        if results is not None:
+            # Display the predictions in a table
+            st.dataframe(results)
+            # Add a button to download the predictions as a CSV file
+            predictions_csv = results.to_csv(index=False)
+            results_file_name = file.name.split(".")[0] + "_b3clf_predictions.csv"
+            st.download_button(
+                "Download predictions as CSV", data=predictions_csv, file_name=results_file_name
+            )

requirements.txt

@@ -7,3 +7,10 @@ openpyxl>=3.0.9
 # rdkit-pypi>=2020.09.1.0
 xgboost==1.4.2
 padelpy>=0.1.11
+rdkit
+
+# for visualization
+streamlit-ketcher
+# for single molecule
+# py3Dmol==2.0.0.post2
+# stmol==0.0.9

test_SMILES.csv

@@ -0,0 +1,7 @@
+[H]OC(=O)C([H])([H])C([H])([H])C([H])([H])N1C([H])([H])C([H])([H])C([H])(OC([H])(c2nc([H])c([H])c([H])c2[H])c2c([H])c([H])c(Cl)c([H])c2[H])C([H])([H])C1([H])[H]
+[H]OC(c1c([H])c([H])c([H])c([H])c1[H])(c1c([H])c([H])c([H])c([H])c1[H])C1([H])C([H])([H])N2C([H])([H])C([H])([H])C1([H])C([H])([H])C2([H])[H]
+[H]c1nc2c(c([H])c1[H])C([H])([H])C([H])([H])c1c([H])c(Cl)c([H])c([H])c1C2=C1C([H])([H])C([H])([H])N(C([H])([H])c2c([H])nc([H])c(C([H])([H])[H])c2[H])C([H])([H])C1([H])[H]
+CCC
+CCCC
+CC(=O)OC1=CC=CC=C1C(=O)O
+CC(=O)OC1=C(C=C(C=C1)Cl)C(=O)OC(=O)C2=C(C=CC(=C2)Cl)OC(=O)C

test_input_sdf.sdf

@@ -0,0 +1,387 @@
+H1_Bepotastine
+     RDKit          3D
+
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+M  END
+>  <compoud_name>  (1)
+H1_Bepotastine
+
+>  <SMILES>  (1)
+[H]OC(=O)C([H])([H])C([H])([H])C([H])([H])N1C([H])([H])C([H])([H])C([H])(OC([H])(c2nc([H])c([H])c([H])c2[H])c2c([H])c([H])c(Cl)c([H])c2[H])C([H])([H])C1([H])[H]
+
+>  <cid>  (1)
+2350
+
+>  <category>  (1)
+N
+
+>  <inchi>  (1)
+InChI=1S/C21H25ClN2O3/c22-17-8-6-16(7-9-17)21(19-4-1-2-12-23-19)27-18-10-14-24(15-11-18)13-3-5-20(25)26/h1-2,4,6-9,12,18,21H,3,5,10-11,13-15H2,(H,25,26)/t21-/m1/s1
+
+>  <Energy>  (1)
+49.1758
+
+$$$$
+H1_Quifenadine
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+
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+ 22 45  1  0
+M  END
+>  <compoud_name>  (2)
+H1_Quifenadine
+
+>  <SMILES>  (2)
+[H]OC(c1c([H])c([H])c([H])c([H])c1[H])(c1c([H])c([H])c([H])c([H])c1[H])C1([H])C([H])([H])N2C([H])([H])C([H])([H])C1([H])C([H])([H])C2([H])[H]
+
+>  <cid>  (2)
+65600
+
+>  <category>  (2)
+N
+
+>  <inchi>  (2)
+InChI=1S/C20H23NO/c22-20(17-7-3-1-4-8-17,18-9-5-2-6-10-18)19-15-21-13-11-16(19)12-14-21/h1-10,16,19,22H,11-15H2/t19-/m1/s1
+
+>  <Energy>  (2)
+84.891
+
+$$$$
+H1_Rupatadine
+     RDKit          3D
+
+ 56 60  0  0  0  0  0  0  0  0999 V2000
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+M  END
+>  <compoud_name>  (3)
+H1_Rupatadine
+
+>  <SMILES>  (3)
+[H]c1nc2c(c([H])c1[H])C([H])([H])C([H])([H])c1c([H])c(Cl)c([H])c([H])c1C2=C1C([H])([H])C([H])([H])N(C([H])([H])c2c([H])nc([H])c(C([H])([H])[H])c2[H])C([H])([H])C1([H])[H]
+
+>  <cid>  (3)
+133017
+
+>  <category>  (3)
+N
+
+>  <inchi>  (3)
+InChI=1S/C26H26ClN3/c1-18-13-19(16-28-15-18)17-30-11-8-20(9-12-30)25-24-7-6-23(27)14-22(24)5-4-21-3-2-10-29-26(21)25/h2-3,6-7,10,13-16H,4-5,8-9,11-12,17H2,1H3
+
+>  <Energy>  (3)
+119.976
+
+$$$$