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通过反应预测和多维优化,加快药物发现中的命中到的进展

David F Nippa¹, Kenneth Atz¹, Yannick Stenzhorn¹

¹Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, Basel, Switzerland.

Nature communications

|November 25, 2025

概括

这项研究通过整合高通量实验和深度学习来加速药物发现,以快速合成新生物活性化合物. 它确定了强大的MAGL抑制剂,显著改善了药物开发时间表.

科学领域:

药用化学医学化学
药物发现药物发现药物发现
计算化学计算化学

背景情况:

加快新生物活性化合物的合成对于药物发现至关重要.
热到的优化阶段是开发新疗法的关键瓶.

研究的目的:

展示一个集成的工作流,将高通量实验 (HTE) 和深度学习结合起来,以加快击中到领先的优化.
通过计算设计和实验验证识别新型,强效的单糖醇脂酶 (MAGL) 抑制剂.

主要方法:

使用HTE生成了13,490个新的Minisci型C-H化反应的数据集.
训练深度图形神经网络来预测反应结果.
执行了基于支架的计数,以创建一个由26,375个分子组成的虚拟库.
使用反应预测,物理化学性质和基于结构的评分来评估虚拟库.
合成和表征了14个主要的MAGL抑制剂候选者.

主要成果:

从虚拟库中确定了212个MAGL抑制剂候选者.
合成了14种表现出亚纳米分子活性的化合物,其功效增加了4500倍.
对已识别的抑制剂实现了有利的药理学概况.
获得了与MAGL设计的三种配体的共同晶体结构,揭示了结合模式.

结论:

集成的工作流大大减少了从成功到成功的循环时间.
结合小型化HTE,深度学习和分子性质优化是药物发现的有效方法.
这种方法可以快速多样化命中和结构,以加速治疗发展.

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