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

TL;DR

This review explores how quantum machine learning, especially quantum neural networks, can advance drug discovery by improving molecular property prediction and generation, while discussing theoretical foundations and practical challenges.

Contribution

It provides a comprehensive overview of quantum machine learning applications in drug discovery, highlighting recent developments and future challenges.

Findings

Quantum neural networks can potentially enhance molecular property prediction.

Hybrid quantum-classical approaches are promising for drug discovery tasks.

The review discusses both benefits and challenges of quantum ML in chemistry.

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.