当前位置:科学网首页 > 小柯机器人 >详情
贝叶斯反应优化在化学合成中的应用
作者:小柯机器人 发布时间:2021/2/8 10:31:51

近日,美国普林斯顿大学教授Abigail G. Doyle及其课题组研究出作为化学合成工具的贝叶斯反应优化。 这一研究成果于2021年2月3日发表在国际顶尖学术期刊《自然》上。

在这里,该课题组报告了贝叶斯反应优化框架和开源软件工具的开发,该工具可让化学家轻松地将最先进的优化算法集成到他们的日常实验室实践中。研究团队收集了钯催化的直接芳基化反应的大型基准数据集,将贝叶斯优化与人为决策相比对,对优化反应进行了系统的研究,并将贝叶斯优化应用于两个现实世界中的优化工作(Mitsunobu和脱氧氟化反应)。

基准测试是通过一款在线游戏完成的,该游戏将专业化学家和工程师所做的决定与实验室中的真实实验联系起来。他们的发现表明,贝叶斯优化在平均优化效率(实验数量)和一致性(结果与初始可用数据的差异)方面均优于人类决策。

总体而言,他们的研究表明,在日常实验室实践中采用贝叶斯优化方法,可以通过对运行的实验进行更明智的、数据驱动的决策,从而促进功能化学品的更有效合成。

研究人员表示,反应优化是合成化学的基础,包括从优化工业工艺的收率到选择制备候选药物的条件。同样,从优化虚拟个人助理到培训社交媒体和产品推荐系统,参数优化在人工智能中无处不在。由于进行实验的成本很高,这两个领域的科学家通过评估可能配置的一小部分来设置大量(超)参数值。

贝叶斯优化,一种基于响应面的迭代全局优化算法,已经在机器学习模型的调优中表现出卓越的性能。贝叶斯优化最近也应用于化学,然而,它在合成化学反应优化中的应用和评价尚未得到研究。

附:英文原文

Title: Bayesian reaction optimization as a tool for chemical synthesis

Author: Benjamin J. Shields, Jason Stevens, Jun Li, Marvin Parasram, Farhan Damani, Jesus I. Martinez Alvarado, Jacob M. Janey, Ryan P. Adams, Abigail G. Doyle

Issue&Volume: 2021-02-03

Abstract: Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates. Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems. Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models. Bayesian optimization has also been recently applied in chemistry; however, its application and assessment for reaction optimization in synthetic chemistry has not been investigated. Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices. We collect a large benchmark dataset for a palladium-catalysed direct arylation reaction, perform a systematic study of Bayesian optimization compared to human decision-making in reaction optimization, and apply Bayesian optimization to two real-world optimization efforts (Mitsunobu and deoxyfluorination reactions). Benchmarking is accomplished via an online game that links the decisions made by expert chemists and engineers to real experiments run in the laboratory. Our findings demonstrate that Bayesian optimization outperforms human decisionmaking in both average optimization efficiency (number of experiments) and consistency (variance of outcome against initially available data). Overall, our studies suggest that adopting Bayesian optimization methods into everyday laboratory practices could facilitate more efficient synthesis of functional chemicals by enabling better-informed, data-driven decisions about which experiments to run.

DOI: 10.1038/s41586-021-03213-y

Source: https://www.nature.com/articles/s41586-021-03213-y

期刊信息

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:42.778
官方网址:http://www.nature.com/