使用多任务学习加速化学反应优化
Connor J Taylor1,2, Kobi C Felton3, Daniel Wigh2,3
1Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, United Kingdom.
ACS central science
|May 30, 2023
概括
多任务贝叶斯优化 (MTBO) 通过使用历史数据加速化学反应优化. 这种机器学习方法有效地优化了用于药物化学的C-H激活反应,降低了成本.
科学领域:
- 药用化学 医学化学
- 化学合成 化学合成
- 机器学习 机器学习
背景情况:
- 对于药物发现而言,C-H键功能化至关重要,但对于极性群体来说具有挑战性.
- 贝叶斯优化 (BO) 已经显示出对自优化化学反应的前景.
- 之前的BO方法缺乏先前的反应知识.
研究的目的:
- 探索多任务贝叶斯优化 (MTBO) 用于使用历史数据加速新的反应优化.
- 将MTBO应用于现实世界的药物化学问题,特别是优化制药中间体.
- 与传统方法相比,证明MTBO的效率和节省成本的潜力.
主要方法:
- 使用多任务贝叶斯优化 (MTBO) 与*in silico*研究的历史反应数据.
- 在基于流动的自主反应器平台上实现了MTBO,用于药用化学应用.
- 在C-H激活反应与多种基质上测试了MTBO.
主要成果:
- 通过利用过去的实验数据,MTBO成功地加速了新反应的优化.
- 该方法有效地优化了使用流体化学的药物中间体的产量.
- MTBO确定了未见的C-H激活反应的最佳条件,证明了它的多功能性.
结论:
- 在药物化学中,MTBO是加速反应优化的有效工具.
- 这种方法代表了利用数据和机器学习用于化学合成的重大进步.
- 在流程优化方面,MTBO具有大幅降低成本的潜力.
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