9i9c
CRYSTAL STRUCTURE OF HUMAN MONOACYLGLYCEROL LIPASE WITH COMPOUND 29
Structural highlights
FunctionMGLL_HUMAN Converts monoacylglycerides to free fatty acids and glycerol. Hydrolyzes the endocannabinoid 2-arachidonoylglycerol, and thereby contributes to the regulation of endocannabinoid signaling, nociperception and perception of pain (By similarity). Regulates the levels of fatty acids that serve as signaling molecules and promote cancer cell migration, invasion and tumor growth.[1] Publication Abstract from PubMedThe 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 evaluated using reaction prediction, physicochemical property assessment, and structure-based scoring, identifying 212 MAGL inhibitor candidates. Of these, 14 compounds were synthesized and exhibited subnanomolar activity, representing a potency improvement of up to 4500 times over the original hit compound. These ligands also showed favorable pharmacological profiles. Co-crystallization of three computationally designed ligands with the MAGL protein provided structural insights into their binding modes. This study demonstrates the potential of combining miniaturized HTE with deep learning and optimization of molecular properties to reduce cycle times in hit-to-lead progression. Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.,Nippa DF, Atz K, Stenzhorn Y, Muller AT, Tosstorff A, Benz J, Binch H, Burkler M, Haider A, Heer D, Hochstrasser R, Kramer C, Reutlinger M, Schneider P, Shema T, Topp A, Walter A, Wittwer MB, Wolfard J, Kuhn B, van der Stelt M, Martin RE, Grether U, Schneider G Nat Commun. 2025 Nov 26. doi: 10.1038/s41467-025-66324-4. PMID:41290653[2] From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine. References
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