Structure-Based Drug Discovery with Deep Learning.

Journal: Chembiochem : a European journal of chemical biology
Published Date:

Abstract

Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep-learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a renaissance in structure-based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.

Authors

  • R Özçelik
    Institute for Complex Molecular Systems and Dept. Biomedical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.
  • D van Tilborg
    Institute for Complex Molecular Systems and Dept. Biomedical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.
  • J Jiménez-Luna
    AI4Science, Microsoft Research, Cambridge, CB1 2FB, UK.
  • F Grisoni
    Institute for Complex Molecular Systems and Dept. Biomedical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.