Data-centric challenges with the application and adoption of artificial intelligence for drug discovery.

Journal: Expert opinion on drug discovery

Published Date: Sep 24, 2024

Abstract

INTRODUCTION: Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and scope of AI models.

Authors

Ghita Ghislat

U1104, CNRS UMR7280, Centre D'Immunologie de Marseille-Luminy, Inserm, Marseille, France.
Saiveth Hernández-Hernández

Cancer Research Center of Marseille (INSERM U1068, Institut Paoli-Calmettes, Aix-Marseille Université UM105, CNRS UMR7258), 13009 Marseille, France.
Chayanit Piyawajanusorn

Department of Bioengineering, Imperial College London, London, UK.
Pedro J Ballester

Cancer Research Center of Marseille, INSERM U1068, Marseille, France; Institut Paoli-Calmettes, Marseille, France; Aix-Marseille Université, Marseille, France; Cancer Research Center of Marseille UMR7258, Marseille, France.

Keywords

Artificial Intelligence Benchmarking Bias Drug Development Drug Discovery Humans Models, Theoretical Uncertainty

External Resources

View on PubMed Access via DOI PubMed (39316009)

Data-centric challenges with the application and adoption of artificial intelligence for drug discovery.

Abstract

Authors

Keywords

External Resources

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