Discovery of a potent TDP1 inhibitor through machine learning-driven predictive modeling combined with structure-based virtual screening and experimental validation.

Tyrosyl-DNA phosphodiesterase I (TDP1) repairs topoisomerase I (TOP1)-mediated DNA damage and is a promising anticancer target, particularly in combination with TOP1 inhibitors. However, the discovery of potent and drug-like TDP1 inhibitors remains challenging due to the limited structural diversity of known active compounds. Here, we developed an integrated computational framework combining machine learning (ML), deep learning (DL), and structure-based docking with experimental validation. A curated dataset of 2040 compounds (857 active, 1183 inactive) was assembled and analyzed by scaffold composition. A total of 40 binary classification models were constructed using six ML algorithms and a deep neural network (DNN), each paired with five molecular fingerprint representations, along with five graph neural network architectures (GCN, GAT, MPNN, AttentiveFP, and FPGNN). The SVM::RDKitDes model performed best (AUC = 0.89, F1 = 0.78, BA = 0.80), with robustness confirmed by Y-scrambling and randomized-split analyses, and SHAP analysis identified 20 key descriptors of TDP1 inhibition. The model was deployed as a web application (http://drugpred.top:5050) and standalone desktop applications (.exe) are available at https://github.com/zenghuang8006/TDP1-inhibitor-prediction. The validated model was applied to screen 201 231 compounds, followed by drug-likeness filtering and hierarchical docking, yielding 16 candidates. Biological evaluation identified compound AO65 as a potent TDP1 inhibitor (IC50 = 0.80 ± 0.02 µM), and quantum chemical calculations and docking elucidated its electronic properties and binding within the catalytic domain. This work demonstrates the value of integrating ML-driven prediction with structure-based approaches and identifies AO65 as a promising lead for further TDP1-focused investigation.