Machine learning for small molecule drug discovery in academia and industry: ML for small molecules drug discovery
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Author / Producer
Date
2023-12
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
Academic and pharmaceutical industry research are both key for progresses in the field of molecular machine learning. Despite common open research questions and long-term goals, the nature and scope of investigations typically differ between academia and industry. Herein, we highlight the opportunities that machine learning models offer to accelerate and improve compound selection. All parts of the model life cycle are discussed, including data preparation, model building, validation, and deployment. Main challenges in molecular machine learning as well as differences between academia and industry are highlighted. Furthermore, application aspects in the design-make-test-analyze cycle are discussed. We close with strategies that could improve collaboration between academic and industrial institutions and will advance the field even further.
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Publication status
published
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Book title
Journal / series
Volume
3
Pages / Article No.
100056
Publisher
Elsevier
Event
Edition / version
Methods
Software
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Date collected
Date created
Subject
Machine learning; Drug discovery; QSAR; Compound properties; Compound optimization; Drug design; Academia; Pharmaceutical industry; Model life cycle; DMTA; Model deployment
Organisational unit
09458 - Riniker, Sereina Z. / Riniker, Sereina Z.