ArticleBioinformatics (Oxford, England)2023
Insights from incorporating quantum computing into drug design workflows.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantum bioinformatics: a systematic review of methods, trends, and challenges.Briefings in bioinformatics · 2026Pooled it
- From Artificial Intelligence to Quantum Computing: Promise and Prudence in the Field of Cardiovascular Medicine.Journal of the American Heart Association · 2026Article
- Quantum Computing in Drug Discovery Techniques, Challenges, and Emerging Opportunities.Current drug discovery technologies · 2026Review
- Quantum medicine: A quantum-mechanical framework for redox biology, disease and precision medicine.Clinical and translational medicine · 2026Review
- Convergent Mechanisms in Virus-Induced Cancers: A Perspective on Classical Viruses, SARS-CoV-2, and AI-Driven Solutions.Infectious disease reports · 2025Article
- A primer for quantum computing and its applications to healthcare and biomedical research.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A hybrid quantum computing pipeline for real world drug discovery.Scientific reports · 2024Article
- Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways.BMC bioinformatics · 2024Article
- Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.Molecular biotechnology · 2024Review
- New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics.Briefings in bioinformatics · 2024Review
- The Future of Drug Development with Quantum Computing.Methods in molecular biology (Clifton, N.J.) · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
motivationWhile 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.
resultsWe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.