Insights from incorporating quantum computing into drug design workflows
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Abstract
Motivation: While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. Results: We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. Availability and implementation: Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. Supplementary information: Supplementary data are available at Bioinformatics online.
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Lau et al. presented a case study with SARS-CoV-2 protease and related mutants using HypaCADD.
“Finally, they presented a case study with SARSCoV-2 protease and related mutants using the model [84].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [84]
HypaCADD incorporates quantum machine learning (QML) with classical docking and molecular dynamics.
“This model incorporates quantum machine learning (QML) with classical docking and molecular dynamics.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [84]
Lau et al. invented a hybrid classical quantum workflow model for drug design entitled HypaCADD.
“Recently, Lau etal. enriched the drug discovery and development area by inventing a hybrid classical quantum workflow model for drug design entitled HypaCADD.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [84]
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Flagged first, then by citations
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Cited by
Sorted by citations
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- Authors
- Lau, B., Emani, P. S., Chapman, J., Yao, L., Lam, T., Merrill, P., Warrell, J., Gerstein, M. B., & Lam, H. Y. K.
- Affiliations
- HypaHealth, HypaHub Inc. , San Jose, CA 95128, USA
- Yale University (US)
- Corresponding author
- Gerstein, Mark B
- Lam, Hugo Y K
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-12-07
- Volume
- 39
- Issue
- 1
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC9825754
- OpenAlex
- W4310959993
- Cited by
- 54
- References
- 57
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 15 · 2023: 10 · 2022: 1
- FWCI
- 5.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (MH116492-05)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- quantum computing, computer-aided drug design, quantum machine learning, protein-ligand binding, point mutations, molecular docking, molecular dynamics
- MeSH terms
- COVID-19, SARS-CoV-2, Computing Methodologies, Humans, Quantum Theory, Software, Drug Design, Molecular Dynamics Simulation, Workflow
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