Quantum mechanics in drug design: Progress, challenges, and future frontiers

Quantum mechanics has revolutionized computational drug discovery by addressing fundamental limitations of classical approaches. Its significance lies in accurately modeling electronic phenomena crucial for drug-target interactions, including polarization, charge transfer, and covalent reactivity, which classical force fields inadequately represent. This comprehensive review examines quantum mechanical methods in pharmaceutical applications from 2020 to 2025. The literature search methodology employed PubMed, Web of Science, and arXiv databases (January 2020-January 2025), focusing on improvements in density functional theory, QM/MM implementations, machine-learned force fields, and alchemical free energy protocols. We critically evaluate 156 primary research articles and 42 review papers, analyzing performance metrics from community benchmarking studies, including SAMPL, GMTKN55, and pharmaceutical consortia datasets. The review encompasses methodological advances, practical applications, and regulatory considerations for quantum-enhanced drug discovery. Quantum mechanical enhancements provide substantial benefits for specific challenging cases rather than universal improvements across all drug discovery applications. Current methodological innovations have significantly improved computational tractability while maintaining chemical accuracy. A critical evaluation of cost-benefit trade-offs reveals that targeted applications to metal-containing systems, covalent modifications, and polarization-dominated interactions yield the highest return on computational investment. Best practices for reproducible implementation and practical method selection guidelines are crucial for the successful integration of a pharmaceutical pipeline.

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Journal
Communicative & Integrative Biology
Published
2025-12-19
DOI
https://doi.org/10.1080/19420889.2025.2603140
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Quantum mechanics in drug design: Progress, challenges, and future frontiers

Sarfaraz K. Niazi
Communicative & Integrative Biology
Machine Learning in Materials Science
article

Quantum mechanics in drug design: Progress, challenges, and future frontiers

Sarfaraz K. Niazi
article en

Abstract

Quantum mechanics has revolutionized computational drug discovery by addressing fundamental limitations of classical approaches. Its significance lies in accurately modeling electronic phenomena crucial for drug-target interactions, including polarization, charge transfer, and covalent reactivity, which classical force fields inadequately represent. This comprehensive review examines quantum mechanical methods in pharmaceutical applications from 2020 to 2025. The literature search methodology employed PubMed, Web of Science, and arXiv databases (January 2020-January 2025), focusing on improvements in density functional theory, QM/MM implementations, machine-learned force fields, and alchemical free energy protocols. We critically evaluate 156 primary research articles and 42 review papers, analyzing performance metrics from community benchmarking studies, including SAMPL, GMTKN55, and pharmaceutical consortia datasets. The review encompasses methodological advances, practical applications, and regulatory considerations for quantum-enhanced drug discovery. Quantum mechanical enhancements provide substantial benefits for specific challenging cases rather than universal improvements across all drug discovery applications. Current methodological innovations have significantly improved computational tractability while maintaining chemical accuracy. A critical evaluation of cost-benefit trade-offs reveals that targeted applications to metal-containing systems, covalent modifications, and polarization-dominated interactions yield the highest return on computational investment. Best practices for reproducible implementation and practical method selection guidelines are crucial for the successful integration of a pharmaceutical pipeline.

Communicative & Integrative BiologyVol. 19(1)
University of Illinois System (US)
Openalex Percentile: Top 66%
Machine Learning in Materials Science
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