Wee1 inhibitor optimization through deep-learning-driven decision making

Abstract

Deep learning has gained increasing attention in recent years, yielding promising results in hit screening and molecular optimization. Herein, we employed an efficient strategy based on multiple deep learning techniques to optimize Wee1 inhibitors, which involves activity interpretation, scaffold-based molecular generation, and activity prediction. Starting from our in-house Wee1 inhibitor GLX0198 (IC50 = 157.9 nM), we obtained three optimized compounds (IC50 = 13.5 nM, 33.7 nM, and 47.1 nM) out of five picked molecules. Further minor modifications on these compounds led to the identification of potent Wee1 inhibitors with desirable inhibitory effects on multiple cancer cell lines. Notably, the best compound 13 exhibited superior cancer cell inhibition, with IC50 values below 100 nM in all tested cancer cells. These results suggest that deep learning can greatly facilitate decision-making at the stage of molecular optimization.