Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries.

Journal: Chemical reviews
Published Date:

Abstract

The nexus of quantum computing and machine learning─quantum machine learning─offers the potential for significant advancements in chemistry. This Review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery. We discuss the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. Applications to drug discovery are highlighted, including molecular property prediction and molecular generation. We provide a balanced perspective, emphasizing both the potential benefits and the challenges that must be addressed.

Authors

  • Anthony M Smaldone
    Department of Chemistry, Yale University, New Haven 06511, Connecticut, United States.
  • Yu Shee
    Department of Chemistry, Yale University, New Haven, Connecticut 06511, Unites States.
  • Gregory W Kyro
    Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499 United States.
  • Chuzhi Xu
    Department of Chemistry, Yale University, New Haven, Connecticut 06511, Unites States.
  • Nam P Vu
    Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States.
  • Rishab Dutta
    Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States.
  • Marwa H Farag
    NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States.
  • Alexey Galda
    Moderna Inc., 325 Binney Street, Cambridge, Massachusetts 02142, United States.
  • Sandeep Kumar
    Cellon S.A., ZAE Robert Steichen, 16 rue Hèierchen, L-4940, Bascharage, Luxembourg.
  • Elica Kyoseva
    NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States.
  • Victor S Batista
    Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499 United States.