[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126743-en":3,"doc-seo-126743-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126743,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules","Fine-tuning GPT-3 is evaluated for predicting the electronic and functional properties of organic molecules. Results show that fine-tuned models can recognize chemically meaningful patterns, differentiate subtle distinctions, and deliver robust predictive performance for molecular property prediction. The study analyzes resilience to information loss caused by missing atoms or chemical groups and to noise introduced by random alterations in atomic identities. Challenges, limitations, and directions for future improvements of GPT-3 in molecular machine-learning tasks are discussed.","University of Birmingham  \nFine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules  \nXie, Zikai; Evangelopoulos, Xenophon; Omar, Ömer H. ; Troisi, Alessandro; Cooper, Andrew I. ; Chen, Linjiang  \nDOI:  \n10.1039/D3SC04610A  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nXie, Z, Evangelopoulos, X, Omar, ÖH, Troisi, A, Cooper, AI & Chen, L 2024, 'Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules', Chemical Science, vol. 15, no. 2, pp. 500- 510. [https://doi.org/10.1039/D3SC04610A](https://doi.org/10.1039/D3SC04610A)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 05. Aug. 2026  \nOpen Access Article . Pu on 05 Decemberblished 2023. Down on 5/14/2024loaded 10:51:19 AM .  \nhicle is licensed under a Creative C mmons A 3 0 U d[ttr .](ttr .)ibution npor e nce.  \nChemical Science  \nEDGE ARTICLE  \nView Article Online View Journal | View Issue  \nCite this: Chem. Sci., 2024, 15, 500  \nAll publication charges for this article have been paid for by the Royal Society of Chemistry  \nReceived 31st August 2023  \nAccepted 4th December 2023 DOI: 10.1039/d3sc04610a[rsc.li/chemical-science](rsc.li/chemical-science)  \nFine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules  \nZikai Xie,a Xenophon Evangelopoulos,a ¨Omer H. Omar,  b Alessandro Troisi,  b Andrew I. Cooper  *a and Linjiang Chen  *c  \nWe evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce via random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues.  \nIntroduction  \nThere has been recent and growing interest in leveraging machine learning (ML) for diverse applications involving organic molecules, such as predicting molecular properties1–6 and usi","cbCaio4ec7Ae0lhK","https://ap.wps.com/l/cbCaio4ec7Ae0lhK","pdf",1131833,1,12,"English","en",105,"# Introduction\n## Machine learning for organic molecules\n## Large language models in chemistry\n## Study focus and evaluation aspects","[{\"question\":\"What is the main goal of fine-tuning GPT-3 in this study?\",\"answer\":\"To evaluate how effectively fine-tuned GPT-3 can predict the electronic and functional properties of organic molecules.\"},{\"question\":\"How do fine-tuned GPT-3 models perform in identifying molecular patterns?\",\"answer\":\"They can successfully identify and distinguish chemically meaningful patterns and detect subtle differences, with robust predictive performance.\"},{\"question\":\"What robustness tests are used to stress the models?\",\"answer\":\"The study tests resilience to information loss from missing atoms or chemical groups and to noise from random alterations in atomic identities.\"}]","Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules | 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is the main goal of fine-tuning GPT-3 in this study?","Question",{"text":75,"@type":76},"To evaluate how effectively fine-tuned GPT-3 can predict the electronic and functional properties of organic molecules.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do fine-tuned GPT-3 models perform in identifying molecular patterns?",{"text":80,"@type":76},"They can successfully identify and distinguish chemically meaningful patterns and detect subtle differences, with robust predictive performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What robustness tests are used to stress the models?",{"text":84,"@type":76},"The study tests resilience to information loss from missing atoms or chemical groups and to noise from random alterations in atomic 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