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Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
Anthony M Smaldone1, Yu Shee1, Gregory W Kyro1
1Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States.
Chemical Reviews
|June 6, 2025
Summary
Quantum machine learning harnesses quantum computing for chemistry, particularly in drug discovery. This review explores quantum neural networks for molecular property prediction and generation, highlighting potential and challenges.
Area of Science:
- Quantum computing and machine learning integration for advanced chemical applications.
- Focus on quantum neural networks within gate-based quantum computing frameworks.
Background:
- Quantum machine learning (QML) presents transformative potential for computational chemistry.
- Drug discovery is a key area where QML can offer significant advantages.
Purpose of the Study:
- To review the potential of quantum neural networks in drug discovery using gate-based quantum computers.
- To discuss the theoretical underpinnings and practical applications of QML in this domain.
Main Methods:
- Exploration of theoretical foundations: data encoding, variational quantum circuits, and hybrid quantum-classical models.
- Review of QML applications specifically tailored for drug discovery processes.
Main Results:
- Identification of QML's capability in molecular property prediction.
- Assessment of QML's potential for molecular generation tasks.
Conclusions:
- Quantum neural networks show promise for accelerating drug discovery.
- Addressing current challenges is crucial for realizing the full potential of QML in chemistry.
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