ACS Medicinal Chemistry Letters Innovations Webinar

ACS Medicinal Chemistry Letters

Innovations Webinar:

AI-assisted scaffold hopping and generative design of synthetically feasible lead analog space

HELD ON THURSDAY, JUNE 17, 2021

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ACS Medicinal Chemistry Letters Innovations Webinar:

AI-assisted scaffold hopping and generative design of synthetically feasible lead analog space

Speaker:

Greg Makara, ChemPass Ltd. (Hungary)

Derivatization Design of Synthetically Accessible Space for Optimization: In Silico Synthesis vs Deep Generative Design.

Published in ACS Med. Chem. Lett. 2021, 12, 185−194.

Moderator:

György M. Keserű, Research Centre for Natural Sciences (Hungary)

AI-assisted scaffold hopping and generative design of synthetically feasible lead analog space

Summary:

Synthesis is typically the most time-consuming step in the DMTA cycle of lead optimization and timelines can only be effectively controlled if the design methodology considers synthetic feasibility and reagent availability. On the other hand, the completeness of ideation impacts the number of optimization cycles required to reach a preclinical candidate. Thus, the ability to generate a comprehensive and synthetically accessible idea set for optimization cycles promises a step change in the lead optimization paradigm. The SynSpace software has been created as a user-friendly design software to assist medicinal and computational chemists in accessing relevant, synthetically feasible chemical space around their lead series. Scaffold hopping, side-chain optimization, different generative design methods, as well as retrosynthesis are all easily carried out with a few clicks of the mouse and without need for cheminformatic or synthetic expertise. Design results are tabulated including full synthetic schemes, reagent data and compound properties for easy ranking and further processing. In this webinar, we will introduce and demo SynSpace, compare its results to that of a deep learning generative design technique, and present a few lead optimization case studies.

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Speakers:

Greg Makara

Greg Makara

CEO

ChemPass Ltd.

Greg Makara completed his PhD in medicinal chemistry at SUNY at Buffalo in 1996 and his postdoctoral studies in medicinal chemistry and molecular modelling with Garland Marshall at the Center for Molecular Design at Washington University at St. Louis in 1998. Since then he has spent >20 years in the pharmaceutical industry, most of it in leadership levels at Neogenesis Pharmaceuticals (Boston, USA), Merck & Co. (Rahway, USA), AMRI Hungary (Hungary), ComInnex (Hungary) and ChemPass (Hungary). His expertise includes organic synthesis, medicinal chemistry, fragment-based drug discovery, drug design, and cheminformatics. He has published >30 papers in peer-reviewed journals and has contributed to 10 patent applications.

György M. Keserű

György M. Keserű

Unit Head, Medicinal Chemistry

RCNS Hungary

György M. Keserű obtained his Ph.D. in Budapest, Hungary. He worked for Sanofi and then moved to Gedeon Richter. In 2007 he was appointed as the Head of Discovery Chemistry. He contributed to the discovery of the antipsychotic Vraylar® (cariprazine) that has been approved and marketed in 2016 in US and EU. He served as a director general of the Research Centre for Natural Sciences (RCNS), Hungary. Since 2015 he is heading the Medicinal Chemistry Research Group at RCNS and is full professor at the Budapest University of Technology and Economics. His research interests include medicinal chemistry and drug design. György was awarded by the Overton and Meyer Award of the European Federation of Medicinal Chemistry and recently was elected as corresponding member of the Hungarian Academy of Sciences.

Agenda

What You Will Learn:

Considering synthetic feasibility in ideation is critical for tight control of synthetic timelines in the DMTA cycle
In silico forward synthesis is a powerful tool for such ideation and cycle time reduction
User-friendly computational tools can alleviate the boundary between medicinal and computational chemists making preclinical research more efficient