人工知能が新薬への道を開く(Artificial intelligence paves way for new medicines)

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3-11-24 ミュンヘン大学(LMU)

LMU、ETH Zurich、Roche Pharma Research and Early Development(pRED)Baselの研究者チームがAIを利用し、薬物分子の合成の最適な方法を予測する手法を開発しました。この手法は実験回数を削減し、化学合成の効率と持続可能性を向上させる可能性があります。
◆通常の薬物成分の合成は化学的に難しく、反応性が低いため、研究者はAIモデルを使用してボリューレーション反応の位置を予測し、最適な条件を提供する方法を開発しました。これにより、既知の薬物成分に追加の活性基を導入する新しい変種を迅速に開発できる可能性があります。

<関連情報>

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
DOI:https://doi.org/10.1038/s41557-023-01360-5

figure 1

Abstract

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.

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有機化学・薬学
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