Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-objective molecular optimization

The rapid and economical synthesis of novel bioactive compounds remains a significant hurdle in drug discovery efforts. This study demonstrates an integrated medicinal chemistry workflow that effectively diversifies hit and lead structures, enabling an efficient acceleration of the critical hit-to-lead optimization phase. Employing high-throughput experimentation (HTE), we generated a comprehensive data set encompassing 13,490 novel Minisci-type C-H alkylation reactions. This data set served as the foundation for training deep graph neural networks to accurately predict reaction outcomes. Scaffold-based enumeration of potential Minisci reaction products, starting from moderate inhibitors of monoacylglycerol lipase (MAGL), yielded a virtual library containing 26,375 molecules. This virtual chemical library was evaluated using reaction prediction, physicochemical property assessment, and structure-based scoring, identifying 212 potential MAGL inhibitor candidates. Of these, 14 ligands were synthesized and exhibited subnanomolar activity, representing a potency improvement of up to 4500 times over the original hit compound. These compounds also displayed favorable pharmacological profiles. Co-crystallization of three computationally designed ligands with the MAGL protein provided valuable structural insights into their preferred binding poses. This study demonstrates the potential of combining miniaturized HTE with deep learning and molecular property optimization to reduce cycle times in drug discovery.