{"title":"Enhanced Regioselectivity Prediction of sp<sup>2</sup> C-H Halogenation via Negative Data Augmentation and Multimodel Integration.","authors":"Zhiting Zhang, Jia Qiu, Jiajun Zheng, Zhunzhun Yu, Lebin Su, Qianghua Lin, Chonghuan Zhang, Kuangbiao Liao","doi":"10.1021/acs.jcim.5c00281","DOIUrl":null,"url":null,"abstract":"<p><p>Efficient molecular editing is pivotal in synthetic chemistry, especially for developing drugs, materials, and high-value chemicals. Electrophilic aromatic substitution (S<sub>E</sub>Ar) reactions, specifically sp<sup>2</sup> C-H halogenation, face significant challenges due to electronic and steric factors, necessitating extensive trial-and-error. This study introduces an innovative machine learning-based model to predict halogenation sites in S<sub>E</sub>Ar reactions, achieving an average accuracy of 93% in 5-fold cross-validation. Employing ensemble techniques, particularly AutoGluon-Tabular (AG), the model demonstrates broad applicability across various aromatic halides, enhancing its utility in drug design, materials science, and more. By reducing experimental uncertainty and optimizing synthetic pathways, this model saves considerable time and resources, thereby accelerating innovation in synthetic chemistry.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jcim.5c00281","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
Abstract
Efficient molecular editing is pivotal in synthetic chemistry, especially for developing drugs, materials, and high-value chemicals. Electrophilic aromatic substitution (SEAr) reactions, specifically sp2 C-H halogenation, face significant challenges due to electronic and steric factors, necessitating extensive trial-and-error. This study introduces an innovative machine learning-based model to predict halogenation sites in SEAr reactions, achieving an average accuracy of 93% in 5-fold cross-validation. Employing ensemble techniques, particularly AutoGluon-Tabular (AG), the model demonstrates broad applicability across various aromatic halides, enhancing its utility in drug design, materials science, and more. By reducing experimental uncertainty and optimizing synthetic pathways, this model saves considerable time and resources, thereby accelerating innovation in synthetic chemistry.
期刊介绍：
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field.
As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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