[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118726-en":3,"doc-seo-118726-105":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},118726,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Machine learning for small molecule drug discovery in academia and industry","Academic and pharmaceutical industry research both drive progress in molecular machine learning, yet investigations often differ in scope and operating context. This work highlights how machine learning models accelerate and improve compound selection by covering the full model life cycle: data preparation, model building, validation, and deployment. Key challenges and differences are discussed alongside practical application in the design-make-test-analyze (DMTA) cycle, and collaboration strategies are proposed to further advance the field.","Machine learning for small molecule drug discovery in academia and industry  \nCitation for published version (APA):  \nVolkamer, A. , Riniker, S. , Nittinger, E. , Lanini, J. , Grisoni, F. , Evertsson, E. , Rodríguez-Pérez, R. , & Schneider, N. (2023) . Machine learning for small molecule drug discovery in academia and industry. Artificial Intelligence in the Life Sciences, 3, Article 100056. [https://doi.org/10.1016/j.ailsci.2022.100056](https://doi.org/10.1016/j.ailsci.2022.100056)  \nDocument license:  \nCC BY-NC-ND  \nDOI:  \n10.1016/j.ailsci.2022.100056  \nDocument status and date:  \nPublished: 01/12/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 02. Aug. 2026  \nArtificial Intelligence in the Life Sciences 3 (2023) 100056  \nContents lists available at ScienceDirect  \nArtificial Intelligence in the Life Sciences  \njournal [homepage: www.elsevier.com/locate/ailsci](homepage: www.elsevier.com/locate/ailsci)  \n| Perspective |  |  |  |\n| --- | --- | --- | --- |\n| Machine learning for small molecule drug discovery in academia and industry |  |  |  |\n| Andrea Volkamer a, Sereina Rinikerb, Eva Nittinger c, Jessica Laninid, Francesca Grisoni e,f, Emma Evertsson c, Raquel Rodríguez-Pérez d,∗, Nadine Schneider d,∗\u003Cbr>a Data Driven Drug Design, Center for Bioinformatics, Saarland University, 66123 Saarbruecken, Germany b Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland\u003Cbr>c Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden d Novartis Institutes for Biomedical Research, Novartis Campus, CH-4002 Basel, Switzerland\u003Cbr>e Eindhoven University of Technology, Dept. Biomedical Engineering, Institute for Complex Molecular Systems, 5600 Eindhoven, the Netherlands f Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, 3584 Utrecht, the Netherlands |  |  |  |\n| a r t i c l e i n f o |  | a b s t r a c t |  |\n| Keywords: Machine learning Drug discovery QSAR\u003Cbr>Compound properties\u003Cbr>Compound optimization Drug design Academia Pharmaceutical industry Model life cycle DMTA\u003Cbr>Model deployment |  | Academic and pharmaceutical industry res","cbCaiiE02TcIakde","https://ap.wps.com/l/cbCaiiE02TcIakde","pdf",2060452,1,11,"English","en",105,"# Introduction\n## QSAR/QSPR and core ML applications\n## Academia vs. industry roles and practices","[{\"question\":\"What problem does the paper address in molecular machine learning for drug discovery?\",\"answer\":\"It examines how machine learning can accelerate and improve compound selection while noting that academia and industry often differ in scope and investigation style.\"},{\"question\":\"Which steps of the model life cycle are discussed?\",\"answer\":\"The paper covers data preparation, model building, validation, and deployment across the full model life cycle.\"},{\"question\":\"How is machine learning used within the design-make-test-analyze cycle?\",\"answer\":\"Application aspects are discussed for supporting decisions throughout the DMTA cycle, aiming to speed discovery and improve the selection of promising molecular entities.\"}]","Machine learning for small molecule drug discovery in academia and industry | 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