DrugAutoML 0.0.2

DrugAutoML: An Open-Source Automated Machine Learning and Statistical Evaluation Tool for Bioactivity Prediction in Drug Discovery

DrugAutoML: An Open-Source Automated Machine Learning and Statistical Evaluation Tool for Bioactivity Prediction in Drug Discovery

Version: 0.0.2
License: MIT

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Overview

DrugAutoML is an open-source Python package designed to automate machine learning (ML) pipelines for bioactivity prediction in drug discovery. Unlike general-purpose AutoML frameworks, DrugAutoML integrates domain-specific data preprocessing, feature engineering, hyperparameter tuning, and model interpretation tailored to chemical and bioactivity datasets. It streamlines the entire workflow—from reading and cleaning raw data to generating interpretable models—making predictive modeling more accessible, reproducible, and transparent.

Key highlights include:

• Automated Data Preprocessing: Cleans and filters chemical data from ChEMBL, ensuring valid SMILES strings, consistent bioactivity units, and user-defined activity thresholds (e.g., IC50).
• Fingerprint Calculation: Computes Extended Connectivity Fingerprints (ECFP4, 2048 bits) using RDKit for robust molecular feature representation.
• Stratified Data Splitting: Performs stratified train–test splits and cross-validation folds to avoid data leakage and maintain class balance.
• Model Selection & Hyperparameter Optimization: Evaluates multiple ML algorithms (e.g., Random Forest, XGBoost, LightGBM, SVC, etc.) via Bayesian optimization (Hyperopt) to maximize accuracy while minimizing computational cost.
• Performance Evaluation & Explainability: Generates a wide range of performance metrics (e.g., ACC, PREC, REC, F1, ROC AUC, PRC AUC) and interprets predictions through SHAP, enabling insight into which molecular features drive bioactivity.
• Automated Reports & Visualizations: Saves classification reports, confusion matrices, ROC & PRC curves, and SHAP plots, providing an end-to-end view of the modeling process.

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Background

Why DrugAutoML?
Traditional drug discovery workflows are resource-intensive, often involving extensive experimental screening of chemical libraries. Machine learning approaches have gained popularity to expedite early-stage screening, but building reliable ML pipelines can be time-consuming and error-prone—especially when dealing with chemical structures and large-scale bioactivity data. General-purpose AutoML frameworks are not fully optimized for drug discovery tasks, lacking specialized preprocessing, domain-specific hyperparameter search spaces, and interpretability tools crucial for identifying relevant chemical features.

How it works

1. Data Input and Preprocessing (Module 1): Loads SMILES and bioactivity data, cleans invalid or ambiguous entries, and applies user-defined thresholds (e.g., IC50 < 100 nM as “Active”).
2. Fingerprint Calculation (Module 2): Converts SMILES to ECFP4 fingerprints for machine-readable chemical representations.
3. Data Splitting (Module 3): Ensures stratified train–test splits and creates cross-validation folds.
4. Model Selection (Module 4): Tests multiple ML models with Bayesian hyperparameter optimization, automatically handling class imbalance.
5. Model Interpretation (Module 5): Evaluates predictive performance (accuracy, precision, recall, etc.) and computes SHAP values to highlight feature contributions.

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Installation

DrugAutoML is available on the Python Package Index (PyPI). To install:

pip install DrugAutoML

## Dependencies

- Python 3.6+  
- RDKit  
- NumPy, Pandas, scikit-learn  
- XGBoost, LightGBM, Hyperopt  
- Matplotlib, Seaborn, SHAP  

*(Most of these will install automatically if not already present.)*

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## Quick Start

Below is a minimal example demonstrating how to use DrugAutoML in a typical workflow. The steps include data loading, preprocessing, fingerprint generation, model selection, and final interpretation.

```python
import DrugAutoML as da

# 1. Define the path to your CSV file and activity thresholds
file_path = "/path/to/your/chembl_data.csv"
ic50_thresholds = {"lower_cutoff": 100, "upper_cutoff": 1000}

# 2. Preprocess the data (removing invalid SMILES, categorizing Active/Inactive)
preprocessed_df = da.load_and_prepare_data(file_path, ic50_thresholds)

# 3. Calculate ECFP4 fingerprints
fingerprint_df = da.smiles_to_fingerprints(preprocessed_df)

# 4. Split the data into training and test sets, and create cross-validation folds
data_dict = da.split_data(test_size=0.2, n_splits=5)
folds = data_dict["folds"]

# 5. Run model selection with Bayesian hyperparameter optimization
#    'auto' runs all supported models, 'max_evals=20' for quick demonstration
results = da.run_model_selection(folds=folds, models_to_run="auto", max_evals=1)

# 6. Interpret a specific model's performance on the test set (e.g., XGBoost)
interpretation_results = da.interpret_model(model_name="XGB")

# 7. Print or log the final results
print("Best Parameters:", interpretation_results["best_params"])
print("Performance on Test Set:", interpretation_results["performance"])

## Detailed Modules

- **data_preprocessing**  
  Cleans CSV data, filters invalid SMILES, and categorizes compounds into Active or Inactive.

- **fingerprint_calculation**  
  Converts SMILES strings into 2048-bit ECFP4 fingerprints using RDKit.

- **data_splitting**  
  Performs stratified train–test splits and cross-validation folds to prevent data leakage.

- **model_selection**  
  Implements Bayesian hyperparameter optimization (Hyperopt) across multiple ML models, logs performance metrics, and saves best parameters.

- **model_interpretation**  
  Re-trains the chosen model on the entire training set, evaluates on the test set, generates classification reports, confusion matrices, ROC & PRC curves, and SHAP plots for interpretability.

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## License

DrugAutoML is released under the MIT License. You are free to use, modify, and distribute this software. We welcome contributions and bug reports.

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## Contributing

Feel free to open an issue or submit a pull request on our GitHub repository. We appreciate feedback and community-driven improvements.

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**Thank you for using DrugAutoML!**  
We hope this tool accelerates your drug discovery research by simplifying and automating critical steps in bioactivity modeling. For any inquiries or collaboration opportunities, please reach out to us via [GitHub Issues](https://github.com/aycapmkcu/DrugAutoML/issues).