Deep learning for low-data drug discovery: Hurdles and opportunities.

Journal: Current opinion in structural biology
Published Date: Apr 25, 2024

Abstract

Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches-which are notoriously 'data-hungry'-might fail to live up to their promise. Developing novel approaches to leverage the power of deep learning in low-data scenarios is sparking great attention, and future developments are expected to propel the field further. This mini-review provides an overview of recent low-data-learning approaches in drug discovery, analyzing their hurdles and advantages. Finally, we venture to provide a forecast of future research directions in low-data learning for drug discovery.

Authors

  • Derek van Tilborg
    Institute for Complex Molecular Systems and Dept. Biomedical Engineering, Eindhoven University of Technology, 5612AZEindhoven, The Netherlands.
  • Helena Brinkmann
    Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands. Electronic address: https://twitter.com/hlnbrkmnn.
  • Emanuele Criscuolo
    Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands. Electronic address: https://twitter.com/emanuelecriscu9.
  • Luke Rossen
    Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands. Electronic address: https://twitter.com/molecular_ml.
  • Rıza Özçelik
    Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey.
  • Francesca Grisoni
    Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, CH-, 8093, Zurich, Switzerland.

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