苏黎世联邦理工大学新加坡SEC公司Schneider Gisbert团队报道了通过几何深度学习的高通量实验实现后期药物多样化。相关研究成果发表在2023年11月23日出版的《自然-化学》期刊上。

后期功能化是优化候选药物性质的一种经济的方法。然而，药物分子的化学复杂性往往使后期多样化具有挑战性。

为了解决这个问题，研究人员开发了一个基于几何深度学习和高通量反应筛选的后期功能化平台。考虑到硼化是后期功能化的关键步骤，计算模型预测了不同反应条件下的反应产率，平均绝对误差为4–5%，而对已知和未知底物的新反应的反应性进行分类，平衡准确度分别为92%和67%。主要产品的区域选择性被准确地捕获，分类器F得分为67%。当应用于23种不同的商业药物分子时，该平台成功地发现了许多结构多样化的机会。量化了空间和电子信息对模型性能的影响，并引入了一种全面、简单、用户友好的反应模式，该模式被证明是无缝集成深度学习和高通量实验进行后期功能化的关键促成因素。

附：英文原文

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

Author: Nippa, David F., Atz, Kenneth, Hohler, Remo, Mller, Alex T., Marx, Andreas, Bartelmus, Christian, Wuitschik, Georg, Marzuoli, Irene, Jost, Vera, Wolfard, Jens, Binder, Martin, Stepan, Antonia F., Konrad, David B., Grether, Uwe, Martin, Rainer E., Schneider, Gisbert

Issue&Volume: 2023-11-23

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.

DOI: 10.1038/s41557-023-01360-5

Source: https://www.nature.com/articles/s41557-023-01360-5