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

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.

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Cited by in corpus (2)

• Hybrid Quantum Generative Adversarial Networks for Molecular Simulation and Drug Discovery
• Quantum computing for genomics: conceptual challenges and practical perspectives