Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning.

Autor: Nippa DF; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.; Department of Pharmacy, Ludwig-Maximilians-Universität München, Munich, Germany., Atz K; Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland., Hohler R; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Müller AT; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Marx A; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Bartelmus C; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Wuitschik G; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Marzuoli I; Process Chemistry and Catalysis (PCC), F. Hoffmann-La Roche Ltd., Basel, Switzerland., Jost V; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Wolfard J; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Binder M; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Stepan AF; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Konrad DB; Department of Pharmacy, Ludwig-Maximilians-Universität München, Munich, Germany. david.konrad@cup.lmu.de., Grether U; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland. uwe.grether@roche.com., Martin RE; Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland. rainer_e.martin@roche.com., Schneider G; Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland. gisbert@ethz.ch.; ETH Singapore SEC Ltd, Singapore, Singapore. gisbert@ethz.ch.
Jazyk: angličtina
Zdroj: Nature chemistry [Nat Chem] 2024 Feb; Vol. 16 (2), pp. 239-248. Date of Electronic Publication: 2023 Nov 23.
DOI: 10.1038/s41557-023-01360-5
Abstrakt: Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in late-stage functionalization, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4-5%, while the reactivity of novel reactions with known and unknown substrates was classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured with a classifier F-score of 67%. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and electronic information on model performance was quantified, and a comprehensive simple user-friendly reaction format was introduced that proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation for late-stage functionalization.
(© 2023. The Author(s).)
Databáze: MEDLINE
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