Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges,Accounts of Chemical Research

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Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges
Accounts of Chemical Research ( IF 16.4 ) Pub Date : 2023-01-30 , DOI: 10.1021/acs.accounts.2c00801
Sukriti Singh ₁ , Raghavan B Sunoj _{1,

2}

Affiliation

In the domain of reaction development, one aims to obtain higher efficacies as measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of outcomes from low to high yields/selectivities is expected. While it is not easy to identify all of the factors that might impact the reaction efficiency, complex and nonlinear dependence on the nature of reactants, catalysts, solvents, etc. is quite likely. Developmental stages of newer reactions would typically offer a few hundreds of samples with variations in participating molecules and/or reaction conditions. These “observations” and their “output” can be harnessed as valuable labeled data for developing molecular machine learning (ML) models. Once a robust ML model is built for a specific reaction under development, it can predict the reaction outcome for any new choice of substrates/catalyst in a few seconds/minutes and thus can expedite the identification of promising candidates for experimental validation. Recent years have witnessed impressive applications of ML in the molecular world, most of them aimed at predicting important chemical or biological properties. We believe that an integration of effective ML workflows can be made richly beneficial to reaction discovery.

中文翻译：

化学催化的分子机器学习：前景与挑战

在反应开发领域，人们的目标是获得更高的效率，以产率和/或选择性衡量。在经验周期中，预计会出现从低到高产量/选择性的混合结果。虽然确定可能影响反应效率的所有因素并不容易，但很可能对反应物、催化剂、溶剂等的性质具有复杂和非线性的依赖性。较新反应的发展阶段通常会提供数百个样品，这些样品的参与分子和/或反应条件各不相同。这些“观察”及其“输出”可以用作开发分子机器学习 (ML) 模型的有价值的标记数据。一旦为正在开发的特定反应建立了强大的 ML 模型，它可以在几秒/分钟内预测任何新选择的底物/催化剂的反应结果，从而可以加快对有希望的候选物的鉴定以进行实验验证。近年来，ML 在分子世界中的应用令人印象深刻，其中大部分旨在预测重要的化学或生物特性。我们相信，有效的 ML 工作流程的集成可以对反应发现大有裨益。

更新日期：2023-01-30

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