Quantum Machine Learning in Drug Discovery: Current State and Challenges
Maria Avramouli, Ilias Κ. Savvas, Anna Vasilaki, Georgia Garani, Apostolos Xenakis
Abstract
The drug discovery process is a time-consuming and quite expensive process. The predictive models of machine learning algorithms have been used efficiently for years in various stages of the drug discovery pipeline. The complexity of these algorithms increases as the size of the molecule increases, adding a single atom to a molecule increases the number of possible combinations. Quantum computers with quantum supremacy can play an important role in complex calculations. Combining the two technologies in practice is a complex endeavor that requires diverse, interdisciplinary teams of scientists working together to be able to integrate the two technologies with the goal of reducing cost and time in drug discovery.