{"title":"CatScore: evaluating asymmetric catalyst design at high efficiency","authors":"Bing Yan and Kyunghyun Cho","doi":"10.1039/D4DD00114A","DOIUrl":null,"url":null,"abstract":"<p >Asymmetric catalysis plays a crucial role in advancing medicine and materials science. However, the prevailing experiment-driven methods for catalyst evaluation are both resource-heavy and time-consuming. To address this challenge, we present CatScore – a learning-centric metric designed for the automatic evaluation of catalyst design models at both instance and system levels. This approach harnesses the power of deep learning to predict product selectivity as a function of reactants and the proposed catalyst. The predicted selectivity serves as a quantitative score, enabling a swift and precise assessment of a catalyst's activity. On an instance level, CatScore's predictions correlate closely with experimental outcomes, demonstrating a Spearman's <em>ρ</em> = 0.84, which surpasses the density functional theory (DFT) based linear free energy relationships (LFERs) metric with <em>ρ</em> = 0.55 and round-trip accuracy metrics at <em>ρ</em> = 0.24. Importantly, when ranking catalyst candidates, CatScore achieves a mean reciprocal ranking significantly superior to traditional LFER methods, marking a considerable reduction in labor and time investments needed to find top-performing catalysts.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00114a?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital discovery","FirstCategoryId":"1085","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2024/dd/d4dd00114a","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Asymmetric catalysis plays a crucial role in advancing medicine and materials science. However, the prevailing experiment-driven methods for catalyst evaluation are both resource-heavy and time-consuming. To address this challenge, we present CatScore – a learning-centric metric designed for the automatic evaluation of catalyst design models at both instance and system levels. This approach harnesses the power of deep learning to predict product selectivity as a function of reactants and the proposed catalyst. The predicted selectivity serves as a quantitative score, enabling a swift and precise assessment of a catalyst's activity. On an instance level, CatScore's predictions correlate closely with experimental outcomes, demonstrating a Spearman's ρ = 0.84, which surpasses the density functional theory (DFT) based linear free energy relationships (LFERs) metric with ρ = 0.55 and round-trip accuracy metrics at ρ = 0.24. Importantly, when ranking catalyst candidates, CatScore achieves a mean reciprocal ranking significantly superior to traditional LFER methods, marking a considerable reduction in labor and time investments needed to find top-performing catalysts.