[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126401-en":3,"doc-seo-126401-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126401,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Studying Noncovalent Interactions in Molecular Systems with Machine Learning - Review","Noncovalent interactions (NCIs) are weak molecular forces whose combined impact strongly governs molecular behavior across diverse domains. Despite the small energy contributions of individual interactions, accurately predicting NCIs remains difficult, often requiring molecular-mechanics-level pairwise energies or computationally demanding quantum electron correlation treatments. This review surveys how machine learning supports NCI study by learning nonlinear relationships, integrating experimental and theoretical data, comparing molecular featurization, evaluating explicit prediction models, and enabling inverse design across molecular scales.","University of Groningen  \nStudying Noncovalent Interactions in Molecular Systems with Machine Learning  \nTretiakov, Serhii; Nigam, AkshatKumar; Pollice, Robert  \nPublished in: Chemical reviews  \nDOI:  \n10.1021/acs.chemrev.4c00893  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nVersion created as part of publication process; publisher's layout; not normally made publicly available  \nPublication date: 2025  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nTretiakov, S. , Nigam, A. , & Pollice, R. (2025) . Studying Noncovalent Interactions in Molecular Systems with Machine Learning. Chemical reviews, 125(12), 5776–5829. Article 4c00893 .  \n[https://doi.org/10.1021/acs.chemrev.4c00893](https://doi.org/10.1021/acs.chemrev.4c00893)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 30-12-2025  \nThis article is licensed under CC-BY 4.0   \n[pubs.acs.org/CR](pubs.acs.org/CR)  Review   \nStudying Noncovalent Interactions in Molecular Systems with Machine Learning  \nPublished as part of Chemical Reviews special issue “Weak Interactions in Chemistry and Biology”. Serhii Tretiakov, * AkshatKumar Nigam, and Robert Pollice *  \n Cite This: [https://doi.org/10.1021/acs.chemrev.4c00893](https://doi.org/10.1021/acs.chemrev.4c00893)  \nRead Online  \nDownloaded via 82.169.4.46 on June 16, 2025 at 12:14:42 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nACCESS  \n Metrics & More  \n Article Recommendations  \nABSTRACT: Noncovalent interactions (NCIs) is an umbrella term for a multitude of typically weak interactions within and between molecules. Despite the low individual energy contributions, their collective effect significantly influences molecular behavior. Accordingly, understanding these interactions is crucial across fields like catalysis, drug design, materials science, and environmental chemistry. However, predicting NCIs is challenging, requiring at least molecular mechanics-level pairwise energy contributions or efficient quantum mechanical electron correlation treatment. In this review, we investigate the application of machine learning (ML) to study NCIs in molecular systems, an emerging research field.  \nML excels at modeling complex nonlinear relationships, and is capable of integrating vast data sets from experimental and theoretical sources. It offers a powerful approach for analyzing interactions across scales, from small molecules to large biomolecular assemblies. Specifically, we examine data sets characterizing NCIs, compare molecular featurization techniques, a","cbCaidSbt015h4Ev","https://ap.wps.com/l/cbCaidSbt015h4Ev","pdf",11727844,7,1,55,"English","en",105,"# Introduction\n## Overview of Noncovalent Interactions\n## General Types of NCIs\n## Identifying the Nature of NCIs\n# Overview of Machine Learning Approaches\n## General Classification\n## Deep Learning\n# Data Sets for Noncovalent Interactions\n## Data Set Accuracy\n## Large Molecular Systems\n## Solid State Molecular Systems\n## Molecular Systems with Heavy Elements","[{\"question\":\"Why are noncovalent interactions important despite being weak?\",\"answer\":\"Noncovalent interactions are individually weak, but their collective effect significantly influences molecular behavior.\"},{\"question\":\"What makes predicting noncovalent interactions challenging?\",\"answer\":\"Accurate prediction typically requires at least molecular-mechanics-level pairwise energy contributions or efficient quantum mechanical treatments of electron correlation.\"},{\"question\":\"How does machine learning help study noncovalent interactions?\",\"answer\":\"Machine learning can model complex nonlinear relationships, integrate large experimental and theoretical datasets, compare featurization methods, predict NCIs, and support inverse design while improving accuracy and reducing computational cost.\"}]","Studying Noncovalent Interactions in Molecular Systems with Machine Learning - 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