Integrating machine learning and deep learning with multiple molecular fingerprints for topoisomerase I inhibitor screening and lead identification.

Abstract

Topoisomerase I (TOP1) is a crucial anticancer target, but the development of traditional TOP1 inhibitors suffers from long research cycles, high costs, and low success rates. Existing artificial intelligence (AI)-driven studies lack systematic comparisons of molecular fingerprints and algorithms, as well as user-friendly predictive application tools. To address these gaps, this study retrieved TOP1 inhibitor activity data from the ChEMBL database, integrated five types of molecular fingerprints (AtomPairs, MACCS, Morgan, PharmacoPFP, and RDKitDes), and constructed and compared classical machine learning (ML) models and deep learning (DL) models, resulting in a total of 40 models. The four top-performing models, SVM::Morgan, RF::Morgan, DNN::MACCS, and KNN::Morgan, achieved ROC-AUC values of 0.93-0.94 under random splitting. Y-scrambling supported that the models learned non-random structure-activity relationships, while SHAP analysis identified key molecular features. The URL of the developed web application is http://drugpred.top:5000 , and this application enables the prediction of TOP1 inhibitory activity via SMILES (Simplified Molecular-Input Line-Entry System) or molecular structure drawing. Additionally, standalone desktop applications (.exe) for offline prediction are freely available at https://github.com/zenghuang8006/TOP1-inhibitor-prediction . Screening of 189,554 SPECS compounds followed by in vitro validation identified AG60 and AI61 as potential TOP1 inhibitors hits, with inhibition rates of 64% and 90% at 400 µM, respectively. Overall, this study provides a practical computational framework for TOP1 inhibitor screening and identifies promising candidate compounds. Notably, scaffold-split AUC values decreased to 0.67-0.82, indicating reduced extrapolative performance for compounds containing previously unseen scaffolds.