Drug Discovery Approaches using Quantum Machine Learning
Special Session (Research Track)
Hosted in Virtual Platform
DescriptionExisting drug discovery pipelines take 5-10 years and cost billions of dollars. Computational approaches such as, Generative Adversarial Networks (GANs) discover drug candidates by generating molecular structures that obey chemical and physical properties and show affinity towards binding with the receptor for a target disease. However, classical GANs cannot explore certain regions of the chemical space and suffer from curse-of-dimensionality. We propose various qubit-efficient hybrid quantum classical machine learning techniques to learn richer representation of molecules more efficiently than classical approaches.