دورية أكاديمية
Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning.
| العنوان: | Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning. |
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| المؤلفون: | 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. |
| المصدر: | Nature chemistry [Nat Chem] 2024 Feb; Vol. 16 (2), pp. 239-248. Date of Electronic Publication: 2023 Nov 23. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101499734 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1755-4349 (Electronic) Linking ISSN: 17554330 NLM ISO Abbreviation: Nat Chem Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London : Nature Pub. Group |
| مواضيع طبية MeSH: | Deep Learning*, High-Throughput Screening Assays |
| مستخلص: | 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).) |
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| معلومات مُعتمدة: | 205321_182176 Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) |
| تواريخ الأحداث: | Date Created: 20231123 Date Completed: 20240209 Latest Revision: 20240210 |
| رمز التحديث: | 20240211 |
| مُعرف محوري في PubMed: | PMC10849962 |
| DOI: | 10.1038/s41557-023-01360-5 |
| PMID: | 37996732 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1755-4349 |
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| DOI: | 10.1038/s41557-023-01360-5 |