Kenneth Atz, David F. Nippa, Alex T. Müller, Vera Jost, Andrea Anelli, Michael Reutlinger, Christian Kramer, Rainer E. Martin, Uwe Grether, Gisbert Schneider and Georg Wuitschik
{"title":"Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry","authors":"Kenneth Atz, David F. Nippa, Alex T. Müller, Vera Jost, Andrea Anelli, Michael Reutlinger, Christian Kramer, Rainer E. Martin, Uwe Grether, Gisbert Schneider and Georg Wuitschik","doi":"10.1039/D4MD00196F","DOIUrl":null,"url":null,"abstract":"<p >Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon–carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an <em>F</em><small><sub>1</sub></small>-score for a binary classification of 79.1% (±0.9%). Validation on eight reactions revealed a receiver operating characteristic (ROC) curve (AUC) value of 0.82 (±0.07) for few-shot machine learning. On the other hand, zero-shot machine learning models achieved a mean ROC-AUC value of 0.63 (±0.16). This study positively advocates the application of few-shot machine learning-guided reaction condition selection for HTE campaigns in medicinal chemistry and highlights practical applications as well as challenges associated with zero-shot machine learning.</p>","PeriodicalId":21462,"journal":{"name":"RSC medicinal chemistry","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"RSC medicinal chemistry","FirstCategoryId":"3","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2024/md/d4md00196f","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon–carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an F1-score for a binary classification of 79.1% (±0.9%). Validation on eight reactions revealed a receiver operating characteristic (ROC) curve (AUC) value of 0.82 (±0.07) for few-shot machine learning. On the other hand, zero-shot machine learning models achieved a mean ROC-AUC value of 0.63 (±0.16). This study positively advocates the application of few-shot machine learning-guided reaction condition selection for HTE campaigns in medicinal chemistry and highlights practical applications as well as challenges associated with zero-shot machine learning.