Description

This toolkit introduces a machine learning pipeline designed for small drug screening. It includes:

1. A Jupyter Notebook for data parsing and preprocessing, model evaluation, optimization, and saving.
2. An Object-Oriented Programming (OOP) script (ml_launcher.py) that uses the trained estimator to make predictions locally on the user’s computer. Users can also modify the notebook to run predictions within a Google Colab environment if desired.

The model is a binary classifier that predicts bioactivity, returning:

• 1 for active compounds (Ki ≤ 50 nM).
• 0 for inactive compounds (Ki > 50 nM).

The example dataset focuses on the 5-HT7 receptor, which demonstrates the workflow but can be adapted to predict bioactivity for any biological target. Notably, the 5-HT7 dataset includes a relatively small number of molecules (177 after preprocessing) and exhibits some class imbalance (active vs. inactive). Consequently, predictions may be suboptimal. Thus, this toolkit serves as an example of a fingerprints-based binary classifier. For other biological targets, different models might perform better. Therefore, it’s crucial to evaluate and select the most suitable estimator for each target.

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Instructions

To run the ml_launcher.py script, ensure the following files are in the same directory:

• ml_launcher.py (script for making predictions).
• best_rfc_model.joblib (the trained estimator).
• example.csv (the file containing molecules to be analyzed).

Requirements Installation

If necessary, install the required Python packages using the following command:

pip install pandas numpy rdkit joblib

Once the required packages are installed, you can run the script in your local environment.

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List of Files

1. 5ht7_IC50.csv - raw dataset from ChEMBL.
2. 5ht7_Ki.csv - raw dataset from ChEMBL.
3. ml_notebook.ipynb - Jupyter Notebook containing the model definition and training pipeline.
4. best_rfc_model.joblib - trained and saved estimator.
5. example.csv - example file with molecules for prediction.
6. ml_launcher.py - python script for running predictions.
7. fps_esti_schema.png - scheme.