Machine learning for small molecule drug discovery in academia and industry - Perspective - Opportunities and challenges across the model life cycle

Sep 17, 2026

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Academic and pharmaceutical industry research both drive progress in molecular machine learning, yet their investigation goals and scope often differ. This Perspective highlights how machine learning models can accelerate and improve compound selection, covering the full model life cycle from data preparation and model building to validation and deployment. Key challenges and academia–industry differences are analyzed, and application considerations in the design–make–test–analyze cycle are discussed. Collaboration strategies to further advance the field are presented.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

Introduction

Bioactivity and property prediction with ML/AI

QSAR and QSPR in drug discovery

Model use in the DMTA cycle

Reproducibility, robustness, and shared practices

Perspective topics

Compound selection opportunities

Model life cycle: data, building, validation, deployment

Challenges and differences between academia and industry

Design–make–test–analyze application aspects

Strategies to improve collaboration

Machine learning for small molecule drug discovery in academia and industry Citation for published version (APA): Volkamer, 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) Document license: CC BY-NC-ND DOI: 10.1016/j.ailsci.2022.100056 Document status and date: Published: 01/12/2023 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • 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. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright 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. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If 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: [www.tue.nl/taverne](www.tue.nl/taverne) Take down policy If you believe that this document breaches copyright please contact us at: [openaccess@tue.nl](openaccess@tue.nl) providing details and we will investigate your claim. Download date: 23. Oct. 2024 Artiﬁcial Intelligence in the Life Sciences 3 (2023) 100056 Contents lists available at ScienceDirect Artiﬁcial Intelligence in the Life Sciences journal [homepage:](homepage: www.elsevier.com/locate/ailsci)[ www.elsevier.com/locate/ailsci](homepage: www.elsevier.com/locate/ailsci) | Perspective | | | | | --- | --- | --- | --- | | Machine learning for small molecule drug discovery in academia and industry | | | | | Andrea Volkamer a, Sereina Rinikerb, Eva Nittinger c, Jessica Laninid, Francesca Grisoni e,f, Emma Evertsson c, Raquel Rodríguez-Pérez d,∗, Nadine Schneider d,∗
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
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
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 | | | | | a r t i c l e i n f o | | a b s t r a c t | | | Keywords: Machine learning Drug discovery QSAR
Compound properties
Compound optimization Drug design Academia Pharmaceutical industry Model life cycle DMTA
Model deployment |

FAQs

What does the Perspective focus on regarding machine learning models in drug discovery?

It focuses on opportunities to accelerate and improve compound selection and explains how machine learning supports the design–make–test–analyze cycle.

Which parts of the model life cycle are discussed?

The discussion covers data preparation, model building, validation, and deployment.

How are academia and industry differences addressed?

Main challenges in molecular machine learning and differences between academia and industry are highlighted, with attention to how models are implemented for decision-making.

Sep 17, 2026

Uploaded by Ivy