Machine Learning-Enhanced Quantitative Structure-Activity Relationship Modeling for DNA Polymerase Inhibitor...

Area of Science:

• Computational chemistry and cheminformatics
• Drug discovery and development
• Molecular biology and oncology

Background:

• Cisplatin resistance in cancer therapy is a major challenge, often mediated by translesion DNA synthesis involving human DNA polymerase η (hpol η).
• Existing small-molecule inhibitors of hpol η, like PNR-7-02, often lack the potency and specificity required to overcome chemoresistance.
• The vast chemical space necessitates advanced computational methods, such as machine learning (ML)-enhanced quantitative structure-activity relationship (QSAR) modeling, for efficient drug discovery.

Purpose of the Study:

• To develop and validate ML-augmented QSAR models for predicting hpol η inhibition.
• To accelerate the discovery of potent and selective indole thio-barbituric acid analogs as hpol η inhibitors.
• To identify novel therapeutic strategies for overcoming cisplatin resistance in cancer treatment.

Main Methods:

• A library of 85 indole thio-barbituric acid analogs with known hpol η inhibition data was curated.
• 220 molecular descriptors (1D-4D) were computed, and 17 ML algorithms were trained and validated using 80% of the data.
• Hyperparameter optimization and 5-fold cross-validation were employed to ensure model robustness and performance evaluation using 14 metrics.

Main Results:

• Ensemble ML methods, particularly random forest, demonstrated exceptional predictive performance (R² > 0.9998) for hpol η inhibition.
• Shapley additive explanations identified electronic properties, lipophilicity, and topological atomic distances as key predictors.
• Nonlinear relationships between molecular descriptors and inhibitory activity were highlighted, with linear models showing higher errors.

Conclusions:

• ML-integrated QSAR modeling offers a powerful and interpretable framework for optimizing hpol η inhibitors.
• This approach significantly accelerates the identification of potent and selective compounds to combat cisplatin resistance.
• The study advances precision oncology by providing a strategy to overcome a critical challenge in cancer therapy.