Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.

ABSTRACT: The rapid and economical synthesis of novel bioactive compounds remains a hurdle in drug discovery efforts. This study demonstrates an integrated medicinal chemistry workflow that effectively diversifies hit and lead structures, enabling an 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. These data 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 eval

SUBMITTER: Nippa DF 

PROVIDER: S-EPMC12749626 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature