幾何学的ディープラーニングを用いたハイスループット実験により、後発医薬品の多様化を可能にする Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning
David F. Nippa,Kenneth Atz,Remo Hohler,Alex T. Müller,Andreas Marx,Christian Bartelmus,Georg Wuitschik,Irene Marzuoli,Vera Jost,Jens Wolfard,Martin Binder,Antonia F. Stepan,David B. Konrad,Uwe Grether,Rainer E. Martin & Gisbert Schneider
Nature Chemistry Published:23 November 2023
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.