Schneider, Nadine, Lanini, Jessica, Rodriguez Perez, Raquel, Grisoni, Francesca, Nittinger, Eva, Riniker, Sereina, Andrea, Volkamer and Emma, Evertsson (2023) Machine Learning for Small Molecule Drug Discovery in Academia and Industry. Artificial Intelligence in the Life Sciences, 3. ISSN 26673185

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 typ- ically differ between academia and industry. Herein, we highlight the op- portunities 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 chal- lenges 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 to potentially improve collaboration between academic and industrial institutions.

Item Type:	Article
Date Deposited:	28 Jan 2023 00:45
Last Modified:	28 Jan 2023 00:45
URI:	https://oak.novartis.com/id/eprint/49127