Add the initial draft

.gitignore

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+# Prerequisites
+*.d
+
+# Compiled object files
+*.slo
+*.lo
+*.o
+*.obj
+
+# Precompiled headers
+*.gch
+*.pch
+
+# Compiled dynamic libraries
+*.so
+*.so.[0-9]*
+*.dylib
+*.dll
+
+# Fortran module files
+*.mod
+*.smod
+
+# Compiled static libraries
+*.lai
+*.la
+*.a
+*.lib
+
+# Executables
+*.exe
+*.out
+*.app
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+.installed.cfg
+MANIFEST
+*.egg-info/
+*.egg
+*.manifest
+*.spec
+pip-log.txt
+pip-delete-this-directory.txt
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.pytest_cache/
+
+# Documentation
+doc/html/
+doc/latex/
+doc/man/
+doc/xml/
+doc/_build/
+doc/source
+doc/modules
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+
+# Editor junk
+tags
+[._]*.s[a-v][a-z]
+[._]*.sw[a-p]
+[._]s[a-v][a-z]
+[._]sw[a-p]
+*~
+\#*\#
+.\#*
+.ropeproject
+.idea/
+.spyderproject
+.spyproject
+.vscode/
+# Mac .DS_Store
+.DS_Store
+
+# jupyter notebook checkpoints
+.ipynb_checkpoints
+
+# version file generated by rob
+B3clf/_version.py
+

.streamlit/config.toml

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+
+[global]
+
+# By default, Streamlit checks if the Python watchdog module is available
+# and, if not, prints a warning asking for you to install it. The watchdog
+# module is not required, but highly recommended. It improves Streamlit's
+# ability to detect changes to files in your filesystem.
+
+# If you'd like to turn off this warning, set this to True.
+
+# Default: false
+# disableWatchdogWarning = false
+
+# By default, Streamlit displays a warning when a user sets both a widget
+# default value in the function defining the widget and a widget value via
+# the widget's key in `st.session_state`.
+
+# If you'd like to turn off this warning, set this to True.
+
+# Default: false
+# disableWidgetStateDuplicationWarning = false
+
+# If True, will show a warning when you run a Streamlit-enabled script
+# via "python my_script.py".
+
+# Default: true
+# showWarningOnDirectExecution = true
+
+# DataFrame serialization.
+
+# Acceptable values:
+# - 'legacy': Serialize DataFrames using Streamlit's custom format. Slow
+# but battle-tested.
+# - 'arrow': Serialize DataFrames using Apache Arrow. Much faster and versatile.
+
+# Default: "arrow"
+dataFrameSerialization = "arrow"
+
+
+[logger]
+
+# Level of logging: 'error', 'warning', 'info', or 'debug'.
+
+# Default: 'info'
+# level = "info"
+
+# String format for logging messages. If logger.datetimeFormat is set,
+# logger messages will default to `%(asctime)s.%(msecs)03d %(message)s`. See
+# [Python's documentation](https://docs.python.org/2.6/library/logging.html#formatter-objects)
+# for available attributes.
+
+# Default: "%(asctime)s %(message)s"
+# messageFormat = "%(asctime)s %(message)s"
+
+
+[client]
+
+# Whether to enable st.cache. This does not affect st.cache_data or
+# st.cache_resource.
+
+# Default: true
+caching = true
+
+# If false, makes your Streamlit script not draw to a
+# Streamlit app.
+
+# Default: true
+# displayEnabled = true
+
+# Controls whether uncaught app exceptions and deprecation warnings
+# are displayed in the browser. By default, this is set to True and
+# Streamlit displays app exceptions and associated tracebacks, and
+# deprecation warnings, in the browser.
+
+# If set to False, deprecation warnings and full exception messages
+# will print to the console only. Exceptions will still display in the
+# browser with a generic error message. For now, the exception type and
+# traceback show in the browser also, but they will be removed in the
+# future.
+
+# Default: true
+# showErrorDetails = true
+
+# Change the visibility of items in the toolbar, options menu,
+# and settings dialog (top right of the app).
+
+# Allowed values:
+# * "auto" : Show the developer options if the app is accessed through
+# localhost or through Streamlit Community Cloud as a developer.
+# Hide them otherwise.
+# * "developer" : Show the developer options.
+# * "viewer" : Hide the developer options.
+# * "minimal" : Show only options set externally (e.g. through
+# Streamlit Community Cloud) or through st.set_page_config.
+# If there are no options left, hide the menu.
+
+# Default: "auto"
+# toolbarMode = "auto"
+
+
+[runner]
+
+# Allows you to type a variable or string by itself in a single line of
+# Python code to write it to the app.
+
+# Default: true
+# magicEnabled = true
+
+# Install a Python tracer to allow you to stop or pause your script at
+# any point and introspect it. As a side-effect, this slows down your
+# script's execution.
+
+# Default: false
+# installTracer = false
+
+# Sets the MPLBACKEND environment variable to Agg inside Streamlit to
+# prevent Python crashing.
+
+# Default: true
+# fixMatplotlib = true
+
+# Run the Python Garbage Collector after each script execution. This
+# can help avoid excess memory use in Streamlit apps, but could
+# introduce delay in rerunning the app script for high-memory-use
+# applications.
+
+# Default: true
+# postScriptGC = true
+
+# Handle script rerun requests immediately, rather than waiting for script
+# execution to reach a yield point. This makes Streamlit much more
+# responsive to user interaction, but it can lead to race conditions in
+# apps that mutate session_state data outside of explicit session_state
+# assignment statements.
+
+# Default: true
+# fastReruns = true
+
+# Raise an exception after adding unserializable data to Session State.
+# Some execution environments may require serializing all data in Session
+# State, so it may be useful to detect incompatibility during development,
+# or when the execution environment will stop supporting it in the future.
+
+# Default: false
+# enforceSerializableSessionState = false
+
+
+[server]
+
+# List of folders that should not be watched for changes. This
+# impacts both "Run on Save" and @st.cache.
+
+# Relative paths will be taken as relative to the current working directory.
+
+# Example: ['/home/user1/env', 'relative/path/to/folder']
+
+# Default: []
+# folderWatchBlacklist = []
+
+# Change the type of file watcher used by Streamlit, or turn it off
+# completely.
+
+# Allowed values:
+# * "auto" : Streamlit will attempt to use the watchdog module, and
+# falls back to polling if watchdog is not available.
+# * "watchdog" : Force Streamlit to use the watchdog module.
+# * "poll" : Force Streamlit to always use polling.
+# * "none" : Streamlit will not watch files.
+
+# Default: "auto"
+# fileWatcherType = "auto"
+
+# Symmetric key used to produce signed cookies. If deploying on multiple replicas, this should
+# be set to the same value across all replicas to ensure they all share the same secret.
+
+# Default: randomly generated secret key.
+# cookieSecret = "59320264f737a53fb01de73458c8849b0b623a7ba8174de8612fd569c2c25035"
+
+# If false, will attempt to open a browser window on start.
+
+# Default: false unless (1) we are on a Linux box where DISPLAY is unset, or
+# (2) we are running in the Streamlit Atom plugin.
+# headless = false
+
+# Automatically rerun script when the file is modified on disk.
+
+# Default: false
+# runOnSave = false
+
+# The address where the server will listen for client and browser
+# connections. Use this if you want to bind the server to a specific address.
+# If set, the server will only be accessible from this address, and not from
+# any aliases (like localhost).
+
+# Default: (unset)
+# address =
+
+# The port where the server will listen for browser connections.
+
+# Default: 8501
+# port = 8501
+
+# The base path for the URL where Streamlit should be served from.
+
+# Default: ""
+# baseUrlPath = ""
+
+# Enables support for Cross-Origin Resource Sharing (CORS) protection, for added security.
+
+# Due to conflicts between CORS and XSRF, if `server.enableXsrfProtection` is on and
+# `server.enableCORS` is off at the same time, we will prioritize `server.enableXsrfProtection`.
+
+# Default: true
+# enableCORS = true
+
+# Enables support for Cross-Site Request Forgery (XSRF) protection, for added security.
+
+# Due to conflicts between CORS and XSRF, if `server.enableXsrfProtection` is on and
+# `server.enableCORS` is off at the same time, we will prioritize `server.enableXsrfProtection`.
+
+# Default: true
+# enableXsrfProtection = true
+
+# Max size, in megabytes, for files uploaded with the file_uploader.
+
+# Default: 200
+maxUploadSize = 2
+
+# Max size, in megabytes, of messages that can be sent via the WebSocket connection.
+
+# Default: 200
+# maxMessageSize = 200
+
+# Enables support for websocket compression.
+
+# Default: false
+# enableWebsocketCompression = false
+
+# Enable serving files from a `static` directory in the running app's directory.
+
+# Default: false
+# enableStaticServing = false
+
+# Server certificate file for connecting via HTTPS.
+# Must be set at the same time as "server.sslKeyFile".
+
+# ['DO NOT USE THIS OPTION IN A PRODUCTION ENVIRONMENT. It has not gone through security audits or performance tests. For the production environment, we recommend performing SSL termination by the load balancer or the reverse proxy.']
+# sslCertFile =
+
+# Cryptographic key file for connecting via HTTPS.
+# Must be set at the same time as "server.sslCertFile".
+
+# ['DO NOT USE THIS OPTION IN A PRODUCTION ENVIRONMENT. It has not gone through security audits or performance tests. For the production environment, we recommend performing SSL termination by the load balancer or the reverse proxy.']
+# sslKeyFile =
+
+
+[browser]
+
+# Internet address where users should point their browsers in order to
+# connect to the app. Can be IP address or DNS name and path.
+
+# This is used to:
+# - Set the correct URL for CORS and XSRF protection purposes.
+# - Show the URL on the terminal
+# - Open the browser
+
+# Default: "localhost"
+# serverAddress = "localhost"
+
+# Whether to send usage statistics to Streamlit.
+
+# Default: true
+# gatherUsageStats = true
+
+# Port where users should point their browsers in order to connect to the
+# app.
+
+# This is used to:
+# - Set the correct URL for CORS and XSRF protection purposes.
+# - Show the URL on the terminal
+# - Open the browser
+
+# Default: whatever value is set in server.port.
+# serverPort = 8501
+
+
+[mapbox]
+
+# Configure Streamlit to use a custom Mapbox
+# token for elements like st.pydeck_chart and st.map.
+# To get a token for yourself, create an account at
+# https://mapbox.com. It's free (for moderate usage levels)!
+
+# Default: ""
+# token = ""
+
+
+[deprecation]
+
+# Set to false to disable the deprecation warning for the file uploader encoding.
+
+# Default: true
+# showfileUploaderEncoding = true
+
+# Set to false to disable the deprecation warning for using the global pyplot instance.
+
+# Default: true
+# showPyplotGlobalUse = true
+
+
+[theme]
+
+# The preset Streamlit theme that your custom theme inherits from.
+# One of "light" or "dark".
+# base = "light"
+
+# Primary accent color for interactive elements.
+# primaryColor =
+
+# Background color for the main content area.
+# backgroundColor =
+
+# Background color used for the sidebar and most interactive widgets.
+# secondaryBackgroundColor =
+
+# Color used for almost all text.
+# textColor =
+
+# Font family for all text in the app, except code blocks. One of "sans serif",
+# "serif", or "monospace".
+font = "sans serif"
+

README.md

@@ -1,10 +1,11 @@
 ---
-title: Debugging
-emoji: 🏆
-colorFrom: purple
-colorTo: gray
+title: B3clf
+emoji: 🏢
+colorFrom: green
+colorTo: pink
 sdk: streamlit
 sdk_version: 1.27.2
+python_version: 3.8
 app_file: app.py
 pinned: false
 license: gpl-3.0

app.py

@@ -0,0 +1,361 @@
+import itertools as it
+import os
+import tempfile
+from io import StringIO
+
+import joblib
+import numpy as np
+import pandas as pd
+import pkg_resources
+# page set up
+import streamlit as st
+from b3clf.descriptor_padel import compute_descriptors
+from b3clf.geometry_opt import geometry_optimize
+from b3clf.utils import get_descriptors, scale_descriptors, select_descriptors
+# from PIL import Image
+from streamlit_extras.let_it_rain import rain
+from streamlit_ketcher import st_ketcher
+
+from utils import generate_predictions, load_all_models
+
+st.cache_data.clear()
+
+st.set_page_config(
+    page_title="BBB Permeability Prediction with Imbalanced Learning",
+    # page_icon="🧊",
+    layout="wide",
+    # initial_sidebar_state="expanded",
+    # menu_items={
+    #     "Get Help": "https://www.extremelycoolapp.com/help",
+    #     "Report a bug": "https://www.extremelycoolapp.com/bug",
+    #     "About": "# This is a header. This is an *extremely* cool app!"
+    # }
+)
+
+
+keep_features = "no"
+keep_sdf = "no"
+classifiers_dict = {
+    "decision tree": "dtree",
+    "kNN": "knn",
+    "logistic regression": "logreg",
+    "XGBoost": "xgb",
+}
+resample_methods_dict = {
+    "random undersampling": "classic_RandUndersampling",
+    "SMOTE": "classic_SMOTE",
+    "Borderline SMOTE": "borderline_SMOTE",
+    "k-means SMOTE": "kmeans_SMOTE",
+    "ADASYN": "classic_ADASYN",
+    "no resampling": "common",
+}
+
+pandas_display_options = {
+    "line_limit": 50,
+}
+mol_features = None
+info_df = None
+results = None
+temp_file_path = None
+all_models = load_all_models()
+
+# Create the Streamlit app
+st.title(":blue[BBB Permeability Prediction with Imbalanced Learning]")
+info_column, upload_column = st.columns(2)
+
+# inatialize the molecule features and info dataframe session state
+if "mol_features" not in st.session_state:
+    st.session_state.mol_features = None
+if "info_df" not in st.session_state:
+    st.session_state.info_df = None
+
+
+# download sample files
+with info_column:
+    st.subheader("About `B3clf`")
+    # fmt: off
+    st.markdown(
+        """
+        `B3clf` is a Python package for predicting the blood-brain barrier (BBB) permeability of small molecules using imbalanced learning. It supports decision tree, XGBoost, kNN, logistical regression and 5 resampling strategies (SMOTE, Borderline SMOTE, k-means SMOTE and ADASYN). The workflow of `B3clf` is summarized as below. The Source code and more details are available at https://github.com/theochem/B3clf. This project is supported by Digital Research Alliance of Canada (originally known as Compute Canada) and NSERC. This project is maintained by QC-Dev comminity. For further information and inquiries please contact us at qcdevs@gmail.com."""
+    )
+    st.text(" \n")
+    # text_body = """
+    # `B3clf` is a Python package for predicting the blood-brain barrier (BBB) permeability of small molecules using imbalanced learning. It supports decision tree, XGBoost, kNN, logistical regression and 5 resampling strategies (SMOTE, Borderline SMOTE, k-means SMOTE and ADASYN). The workflow of `B3clf` is summarized as below. The Source code and more details are available at https://github.com/theochem/B3clf.
+    # """
+    # st.markdown(f"<p align="justify">{text_body}</p>",
+    #             unsafe_allow_html=True)
+
+    # image = Image.open("images/b3clf_workflow.png")
+    # st.image(image=image, use_column_width=True)
+
+    # image_path = "images/b3clf_workflow.png"
+    # image_width_percent = 80
+    # info_column.markdown(
+    #     f"<img src="{image_path}" style="max-width: {image_width_percent}%; height: auto;">",
+    #     unsafe_allow_html=True
+    #     )
+
+    # fmt: on
+    sdf_col, smi_col = st.columns(2)
+    with sdf_col:
+        # uneven columns
+        # st.columns((2, 1, 1, 1))
+        # two subcolumns for sample input files
+        # download sample sdf
+        # st.markdown(" \n \n")
+        with open("sample_input.sdf", "r") as file_sdf:
+            btn = st.download_button(
+                label="Download SDF sample file",
+                data=file_sdf,
+                file_name="sample_input.sdf",
+            )
+    with smi_col:
+        with open("sample_input_smiles.csv", "r") as file_smi:
+            btn = st.download_button(
+                label="Download SMILES sample file",
+                data=file_smi,
+                file_name="sample_input_smiles.csv",
+            )
+
+# Create a file uploader
+with upload_column:
+    st.subheader("Model Selection")
+    with st.container():
+        algorithm_col, resampler_col = st.columns(2)
+        # algorithm and resampling method selection column
+        with algorithm_col:
+            classifier = st.selectbox(
+                label="Classification Algorithm:",
+                options=("XGBoost", "kNN", "decision tree", "logistic regression"),
+            )
+        with resampler_col:
+            resampler = st.selectbox(
+                label="Resampling Method:",
+                options=(
+                    "ADASYN",
+                    "random undersampling",
+                    "Borderline SMOTE",
+                    "k-means SMOTE",
+                    "SMOTE",
+                    "no resampling",
+                ),
+            )
+
+        # horizontal line
+        st.divider()
+        # upload_col, submit_job_col = st.columns((2, 1))
+        upload_col, _, submit_job_col, _ = st.columns((4, 0.05, 1, 0.05))
+        # upload file column
+        with upload_col:
+            # session state tracking of the file uploader
+            if "uploaded_file" not in st.session_state:
+                st.session_state.uploaded_file = None
+            if "uploaded_file_changed" not in st.session_state:
+                st.session_state.uploaded_file_changed = False
+
+            # def update_uploader_session_info():
+            #     """Update the session state of the file uploader."""
+            #     st.session_state.uploaded_file = uploaded_file
+
+            uploaded_file = st.file_uploader(
+                label="Upload a CSV, SDF, TXT or SMI file",
+                type=["csv", "sdf", "txt", "smi"],
+                help="Input molecule file only supports *.csv, *.sdf, *.txt and *.smi.",
+                accept_multiple_files=False,
+                # key="uploaded_file",
+                # on_change=update_uploader_session_info,
+            )
+
+            if uploaded_file:
+                # st.write(f"the uploaded file: {uploaded_file}")
+                # when new file is uploaded is different from the previous one
+                if st.session_state.uploaded_file != uploaded_file:
+                    st.session_state.uploaded_file_changed = True
+                else:
+                    st.session_state.uploaded_file_changed = False
+                st.session_state.uploaded_file = uploaded_file
+                # when new file is the same as the previous one
+                # else:
+                #     st.session_state.uploaded_file_changed = False
+                # st.session_state.uploaded_file = uploaded_file
+
+            # set session state for the file uploader
+            # st.write(f"the state of uploaded file: {st.session_state.uploaded_file}")
+            # st.write(f"the state of uploaded file changed: {st.session_state.uploaded_file_changed}")
+
+        # submit job column
+        with submit_job_col:
+            st.text(" \n")
+            st.text(" \n")
+            st.markdown(
+                "<div style='display: flex; justify-content: center;'>",
+                unsafe_allow_html=True,
+            )
+            submit_job_button = st.button(
+                label="Submit Job", type="secondary", key="job_button"
+            )
+        # submit_job_col.markdown("<div style="display: flex; justify-content: center;">",
+        #                         unsafe_allow_html=True)
+        # submit_job_button = submit_job_col.button(
+        #     label="Submit job", key="submit_job_button", type="secondary"
+        # )
+        # submit_job_col.markdown("</div>", unsafe_allow_html=True)
+
+
+# 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)
+
+
+feature_column, prediction_column = st.columns(2)
+with feature_column:
+    st.subheader("Molecular Features")
+
+    placeholder_features = st.empty()
+    # placeholder_features = pd.DataFrame(index=[1, 2, 3, 4],
+    #                                     columns=["ID", "nAcid", "ALogP", "Alogp2",
+    #                                              "AMR", "naAromAtom", "nH", "nN"])
+    # st.dataframe(placeholder_features)
+    # placeholder_features.text("molecular features")
+
+with prediction_column:
+    st.subheader("Predictions")
+    # placeholder_predictions = st.empty()
+    # placeholder_predictions.text("prediction")
+
+
+st.write(
+    f"the state of uploaded file changed before checking: {st.session_state.uploaded_file_changed}"
+)
+# Generate predictions when the user uploads a file
+# if submit_job_button:
+
+# if "job_button" in st.session_state:
+#     when new file is uploaded
+#     update_uploader_session_info()
+#     st.write(
+#         f"the state of uploaded file changed after checking: {st.session_state.uploaded_file_changed}"
+#     )
+#     if st.session_state.uploaded_file_changed:
+#         temp_dir = tempfile.mkdtemp()
+#         # Create a temporary file path for the uploaded file
+#         temp_file_path = os.path.join(temp_dir, uploaded_file.name)
+#         # Save the uploaded file to the temporary file path
+#         with open(temp_file_path, "wb") as temp_file:
+#             temp_file.write(uploaded_file.read())
+
+#         mol_features, info_df, results = generate_predictions(
+#             input_fname=temp_file_path,
+#             sep="\s+|\t+",
+#             clf=classifiers_dict[classifier],
+#             _models_dict=all_models,
+#             sampling=resample_methods_dict[resampler],
+#             time_per_mol=120,
+#             mol_features=None,
+#             info_df=None,
+#         )
+#         st.session_state.mol_features = mol_features
+#         st.session_state.info_df = info_df
+#     else:
+#         mol_features, info_df, results = generate_predictions(
+#             input_fname=None,
+#             sep="\s+|\t+",
+#             clf=classifiers_dict[classifier],
+#             _models_dict=all_models,
+#             sampling=resample_methods_dict[resampler],
+#             time_per_mol=120,
+#             mol_features=st.session_state.mol_features,
+#             info_df=st.session_state.info_df,
+#         )
+if submit_job_button and uploaded_file:
+    temp_dir = tempfile.mkdtemp()
+    # Create a temporary file path for the uploaded file
+    temp_file_path = os.path.join(temp_dir, uploaded_file.name)
+    # Save the uploaded file to the temporary file path
+    with open(temp_file_path, "wb") as temp_file:
+        temp_file.write(uploaded_file.read())
+    mol_features, info_df, results = generate_predictions(
+        input_fname=temp_file_path,
+        sep="\s+|\t+",
+        clf=classifiers_dict[classifier],
+        _models_dict=all_models,
+        sampling=resample_methods_dict[resampler],
+        time_per_mol=120,
+        mol_features=None,
+        info_df=None,
+    )
+
+    # feture table
+    with feature_column:
+        if mol_features is not None:
+            selected_feature_rows = np.min(
+                [mol_features.shape[0], pandas_display_options["line_limit"]]
+            )
+            st.dataframe(mol_features.iloc[:selected_feature_rows, :], hide_index=False)
+            # placeholder_features.dataframe(mol_features, hide_index=False)
+            feature_file_name = uploaded_file.name.split(".")[0] + "_b3clf_features.csv"
+            features_csv = mol_features.to_csv(index=True)
+            st.download_button(
+                "Download features as CSV",
+                data=features_csv,
+                file_name=feature_file_name,
+            )
+    # prediction table
+    with prediction_column:
+        # st.subheader("Predictions")
+        if results is not None:
+            # Display the predictions in a table
+            selected_result_rows = np.min(
+                [results.shape[0], pandas_display_options["line_limit"]]
+            )
+            results_df_display = results.iloc[:selected_result_rows, :].style.format(
+                {"B3clf_predicted_probability": "{:.6f}".format}
+            )
+            st.dataframe(results_df_display, hide_index=True)
+            # Add a button to download the predictions as a CSV file
+            predictions_csv = results.to_csv(index=True)
+            results_file_name = (
+                uploaded_file.name.split(".")[0] + "_b3clf_predictions.csv"
+            )
+            st.download_button(
+                "Download predictions as CSV",
+                data=predictions_csv,
+                file_name=results_file_name,
+            )
+            # indicate the success of the job
+            # rain(
+            #     emoji="🎈",
+            #     font_size=54,
+            #     falling_speed=5,
+            #     animation_length=10,
+            # )
+    st.balloons()
+
+
+# hide footer
+# https://github.com/streamlit/streamlit/issues/892
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+# add google analytics
+st.markdown(
+    """
+    <!-- Google tag (gtag.js) -->
+    <script async src="https://www.googletagmanager.com/gtag/js?id=G-WG8QYRELP9"></script>
+    <script>
+      window.dataLayer = window.dataLayer || [];
+      function gtag(){dataLayer.push(arguments);}
+      gtag("js", new Date());
+
+      gtag("config", "G-WG8QYRELP9");
+    </script>
+    """,
+    unsafe_allow_html=True,
+)

packages.txt

@@ -0,0 +1 @@
+default-jre

requirements.txt

@@ -0,0 +1,17 @@
+numpy==1.24.4
+scipy==1.10.1
+scikit-learn==0.24.2
+joblib==1.3.2
+pandas==2.0.3
+openpyxl==3.1.2
+xgboost==1.4.2
+padelpy>=0.1.11
+rdkit==2023.03.3
+# streamlit-extra==0.3.4
+git+https://github.com/arnaudmiribel/streamlit-extras@v0.3.4
+# for visualization
+streamlit-ketcher
+# for single molecule
+# py3Dmol==2.0.0.post2
+# stmol==0.0.9
+git+https://github.com/theochem/B3clf.git

sample_input.sdf

@@ -0,0 +1,387 @@
+H1_Bepotastine
+     RDKit          3D
+
+ 52 54  0  0  1  0  0  0  0  0999 V2000
+    6.2601    3.8627   -0.7580 Cl  0  0  0  0  0  0  0  0  0  0  0  0
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+ 23 48  1  0
+ 24 27  1  0
+ 24 49  1  0
+ 26 27  2  0
+ 26 50  1  0
+ 27 51  1  0
+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
+
+$$$$
+H1_Quifenadine
+     RDKit          3D
+
+ 45 48  0  0  1  0  0  0  0  0999 V2000
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+ 21 44  1  0
+ 22 45  1  0
+M  END
+>  <compoud_name>  (2)
+H1_Quifenadine
+
+>  <SMILES>  (2)
+[H]OC(c1c([H])c([H])c([H])c([H])c1[H])(c1c([H])c([H])c([H])c([H])c1[H])C1([H])C([H])([H])N2C([H])([H])C([H])([H])C1([H])C([H])([H])C2([H])[H]
+
+>  <cid>  (2)
+65600
+
+>  <category>  (2)
+N
+
+>  <inchi>  (2)
+InChI=1S/C20H23NO/c22-20(17-7-3-1-4-8-17,18-9-5-2-6-10-18)19-15-21-13-11-16(19)12-14-21/h1-10,16,19,22H,11-15H2/t19-/m1/s1
+
+>  <Energy>  (2)
+84.891
+
+$$$$
+H1_Rupatadine
+     RDKit          3D
+
+ 56 60  0  0  0  0  0  0  0  0999 V2000
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+    3.2715    1.4705    1.4385 C   0  0  0  0  0  0  0  0  0  0  0  0
+    4.8769    1.3617   -0.8210 C   0  0  0  0  0  0  0  0  0  0  0  0
+    2.4290   -3.7014   -0.8308 C   0  0  0  0  0  0  0  0  0  0  0  0
+    4.3729    2.3200    1.3344 C   0  0  0  0  0  0  0  0  0  0  0  0
+    5.1670    2.2679    0.1982 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.1566    1.0467    0.4633 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.3042    0.2290   -1.7686 C   0  0  0  0  0  0  0  0  0  0  0  0
+    2.2947   -4.4730    0.3198 C   0  0  0  0  0  0  0  0  0  0  0  0
+    1.9875   -3.8347    1.5112 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.4311    0.5316    0.7094 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -7.0633   -0.1364   -0.3325 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -7.0626    0.6338    2.0605 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -0.5731   -1.7154    0.8560 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.1596   -0.4557    1.9235 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -0.2119    1.9818    1.6961 H   0  0  0  0  0  0  0  0  0  0  0  0
+    0.9793    2.3217    0.4489 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.4699   -0.5848   -1.1284 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.7127   -0.8992    0.0866 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.2287    3.0211   -0.2712 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -0.5727    1.7824   -1.3473 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.8776    1.1445   -2.1102 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -3.3405    2.5558   -1.1674 H   0  0  0  0  0  0  0  0  0  0  0  0
+    3.6660    0.0536   -2.8120 H   0  0  0  0  0  0  0  0  0  0  0  0
+    4.5182   -1.1506   -1.8447 H   0  0  0  0  0  0  0  0  0  0  0  0
+    2.4771   -2.0361   -2.8582 H   0  0  0  0  0  0  0  0  0  0  0  0
+    1.4795   -0.8292   -2.0837 H   0  0  0  0  0  0  0  0  0  0  0  0
+    2.6674    1.5029    2.3444 H   0  0  0  0  0  0  0  0  0  0  0  0
+    5.5326    1.3154   -1.6888 H   0  0  0  0  0  0  0  0  0  0  0  0
+    2.6741   -4.1805   -1.7747 H   0  0  0  0  0  0  0  0  0  0  0  0
+    4.6043    3.0064    2.1437 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -4.6110    1.5606    1.2526 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -4.9162    0.0859   -2.7735 H   0  0  0  0  0  0  0  0  0  0  0  0
+    2.4295   -5.5486    0.2902 H   0  0  0  0  0  0  0  0  0  0  0  0
+    1.8762   -4.3969    2.4339 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -8.0471   -0.5796   -0.2022 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -8.1536    0.6818    1.9793 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.7913   -0.2348    2.6683 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.7355    1.5422    2.5773 H   0  0  0  0  0  0  0  0  0  0  0  0
+  1 23  1  0
+  2  8  1  0
+  2  9  1  0
+  2 11  1  0
+  3 13  2  0
+  3 27  1  0
+  4 25  2  0
+  4 29  1  0
+  5  6  1  0
+  5  7  1  0
+  5 10  2  3
+  6  8  1  0
+  6 31  1  0
+  6 32  1  0
+  7  9  1  0
+  7 33  1  0
+  7 34  1  0
+  8 35  1  0
+  8 36  1  0
+  9 37  1  0
+  9 38  1  0
+ 10 12  1  0
+ 10 13  1  0
+ 11 18  1  0
+ 11 39  1  0
+ 11 40  1  0
+ 12 14  2  0
+ 12 19  1  0
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+ 14 15  1  0
+ 14 20  1  0
+ 15 16  1  0
+ 15 41  1  0
+ 15 42  1  0
+ 16 17  1  0
+ 16 43  1  0
+ 16 44  1  0
+ 17 21  2  0
+ 18 24  2  0
+ 18 25  1  0
+ 19 22  2  0
+ 19 45  1  0
+ 20 23  2  0
+ 20 46  1  0
+ 21 26  1  0
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+ 28 29  2  0
+ 28 30  1  0
+ 29 53  1  0
+ 30 54  1  0
+ 30 55  1  0
+ 30 56  1  0
+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
+
+$$$$

sample_input_smiles.csv

@@ -0,0 +1,6 @@
+OC(=O)CCCN1CCC(OC(c2ncccc2)c2ccc(Cl)cc2)CC1
+OC(c1ccccc1)(c1ccccc1)C1CN2CCC1CC2
+c1nc2c(cc1)CCc1cc(Cl)ccc1C2=C1CCN(Cc2cncc(C)c2)CC1
+C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N
+CC(=O)Oc1ccccc1C(=O)O
+CC(=O)Oc1c(cc(cc1)Cl)C(=O)OC(=O)c1c(ccc(c1)Cl)OC(=O)C

utils.py

@@ -0,0 +1,155 @@
+import itertools as it
+import os
+
+import joblib
+import numpy as np
+import pandas as pd
+import pkg_resources
+import streamlit as st
+from b3clf.descriptor_padel import compute_descriptors
+from b3clf.geometry_opt import geometry_optimize
+from b3clf.utils import get_descriptors, scale_descriptors, select_descriptors
+
+
+@st.cache_resource()
+def load_all_models():
+    """Get b3clf fitted classifier"""
+    clf_list = ["dtree", "knn", "logreg", "xgb"]
+    sampling_list = [
+        "borderline_SMOTE",
+        "classic_ADASYN",
+        "classic_RandUndersampling",
+        "classic_SMOTE",
+        "kmeans_SMOTE",
+        "common",
+    ]
+
+    model_dict = {}
+    package_name = "b3clf"
+
+    for clf_str, sampling_str in it.product(clf_list, sampling_list):
+        # joblib_fpath = os.path.join(
+        #     dirname, "pre_trained", "b3clf_{}_{}.joblib".format(clf_str, sampling_str))
+        # pred_model = joblib.load(joblib_fpath)
+        joblib_path_str = f"pre_trained/b3clf_{clf_str}_{sampling_str}.joblib"
+        with pkg_resources.resource_stream(package_name, joblib_path_str) as f:
+            pred_model = joblib.load(f)
+
+        model_dict[clf_str + "_" + sampling_str] = pred_model
+
+    return model_dict
+
+
+@st.cache_resource
+def predict_permeability(
+    clf_str, sampling_str, _models_dict, mol_features, info_df, threshold="none"
+):
+    """Compute permeability prediction for given feature data."""
+    # load the model
+    # pred_model = load_all_models()[clf_str + "_" + sampling_str]
+    pred_model = _models_dict[clf_str + "_" + sampling_str]
+
+    # load the threshold data
+    package_name = "b3clf"
+    with pkg_resources.resource_stream(package_name, "data/B3clf_thresholds.xlsx") as f:
+        df_thres = pd.read_excel(f, index_col=0, engine="openpyxl")
+
+    # default threshold is 0.5
+    label_pool = np.zeros(mol_features.shape[0], dtype=int)
+
+    if type(mol_features) == pd.DataFrame:
+        if mol_features.index.tolist() != info_df.index.tolist():
+            raise ValueError("Features_df and Info_df do not have the same index.")
+
+    # get predicted probabilities
+    info_df.loc[:, "B3clf_predicted_probability"] = pred_model.predict_proba(
+        mol_features
+    )[:, 1]
+    # get predicted label from probability using the threshold
+    mask = np.greater_equal(
+        info_df["B3clf_predicted_probability"].to_numpy(),
+        # df_thres.loc[clf_str + "-" + sampling_str, threshold])
+        df_thres.loc["xgb-classic_ADASYN", threshold],
+    )
+    label_pool[mask] = 1
+
+    # save the predicted labels
+    info_df["B3clf_predicted_label"] = label_pool
+    info_df.reset_index(inplace=True)
+
+    return info_df
+
+
+@st.cache_resource
+def generate_predictions(
+    input_fname: str = None,
+    sep: str = "\s+|\t+",
+    clf: str = "xgb",
+    _models_dict: dict = None,
+    keep_sdf: str = "no",
+    sampling: str = "classic_ADASYN",
+    time_per_mol: int = 120,
+    mol_features: pd.DataFrame = None,
+    info_df: pd.DataFrame = None,
+):
+    """
+    Generate predictions for a given input file.
+    """
+    if mol_features is None and info_df is None:
+        # mol_tag = os.path.splitext(uploaded_file.name)[0]
+        # uploaded_file = uploaded_file.read().decode("utf-8")
+        mol_tag = os.path.basename(input_fname).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=input_fname, 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,
+        )
+
+        # Get computed descriptors
+        mol_features, info_df = get_descriptors(df=df_features)
+
+        # Select descriptors
+        mol_features = select_descriptors(df=mol_features)
+
+        # Scale descriptors
+        mol_features.iloc[:, :] = scale_descriptors(df=mol_features)
+
+        # this is problematic for using the same file for calculation
+        if os.path.exists(internal_sdf) and keep_sdf == "no":
+            os.remove(internal_sdf)
+
+    # Get classifier
+    # clf = get_clf(clf_str=clf, sampling_str=sampling)
+    # Get classifier
+    result_df = predict_permeability(
+        clf_str=clf,
+        sampling_str=sampling,
+        _models_dict=_models_dict,
+        mol_features=mol_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]
+    ]
+
+    return mol_features, info_df, result_df