Machine Learning-Enhanced Quantitative Structure-Activity Relationship Modeling for DNA Polymerase Inhibitor Discovery: Algorithm Development and Validation.

ABSTRACT:

Background

Cisplatin resistance remains a significant obstacle in cancer therapy, frequently driven by translesion DNA synthesis mechanisms that use specialized polymerases such as human DNA polymerase η (hpol η). Although small-molecule inhibitors such as PNR-7-02 have demonstrated potential in disrupting hpol η activity, current compounds often lack sufficient potency and specificity to effectively combat chemoresistance. The vastness of chemical space further limits traditional drug discovery approaches, underscoring the need for advanced computational strategies such as machine learning (ML)-enhanced quantitative structure-activity relationship (QSAR) modeling.

Objective

This study aimed to develop and validate ML-augmented QSAR models to accurately predict hpol η inhibi

SUBMITTER: Kakraba S 

PROVIDER: S-EPMC12675996 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature