Discovery of WRN helicase inhibitors by 3D-CNN docking and ML consensus from traditional Chinese medicine monomers.

The Werner syndrome (WRN) helicase is a validated synthetic-lethal vulnerability in cancers with microsatellite instability (MSI), making WRN inhibition a potential target for cancer treatment. Therefore, a computer-aided drug discovery (CADD) pipeline integrating deep learning-based molecular docking and machine learning classification was employed to identify WRN inhibitors from Traditional Chinese Medicine (TCM) monomers. A library of 2940 TCM monomers was initially filtered by Lipinski's Rule of 5, GNINA deep learning framework utilized 3D Convolutional Neural Networks (3D-CNNs) to capture complex spatial interaction patterns to identify candidates against the WRN D1/D2 interface with high AI confidence and thermodynamic affinity. These hits were further validated through an ensemble of ML classifiers (Random Forest, XGBoost, and SVM). Three promising candidates, including okanin, nordihydroguaiaretic acid (NDGA), and desmethylglycitein were identified as top-ranking hits based on consensus scoring across the two-stage screening pipeline. However, subsequent extended molecular dynamics (MD) simulations and MM/PBSA free energy calculations revealed distinct results for these scaffolds. While static docking ranked desmethylglycitein highly, it suffered a severe drop in theoretical affinity over the simulation timeframe, highlighting the workflow's utility in filtering false positives. In contrast, NDGA emerged as the most thermodynamically stable test compound, driven by strong van der Waals interactions and high-density hydrogen bond formation. Collectively, these results demonstrate the utility of AI-driven virtual screening for modernizing TCM-derived drug discovery and nominate NDGA as a potential lead for WRN-targeted cancer therapy.