{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T07:29:50Z","timestamp":1784359790734,"version":"3.55.0"},"reference-count":59,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["MH116492-05"],"award-info":[{"award-number":["MH116492-05"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Jupyter Notebooks with Python code are freely available for academic use on GitHub: https:\/\/www.github.com\/hypahub\/hypacadd_notebook.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac789","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T12:34:17Z","timestamp":1670416457000},"source":"Crossref","is-referenced-by-count":44,"title":["Insights from incorporating quantum computing into drug design\u00a0workflows"],"prefix":"10.1093","volume":"39","author":[{"given":"Bayo","family":"Lau","sequence":"first","affiliation":[{"name":"HypaHealth, HypaHub Inc. , San Jose, CA 95128, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prashant S","family":"Emani","sequence":"additional","affiliation":[{"name":"Program in Computational Biology and Bioinformatics, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jackson","family":"Chapman","sequence":"additional","affiliation":[{"name":"Program in Computational Biology and Bioinformatics, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijing","family":"Yao","sequence":"additional","affiliation":[{"name":"HypaHealth, HypaHub Inc. , San Jose, CA 95128, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarsus","family":"Lam","sequence":"additional","affiliation":[{"name":"HypaHealth, HypaHub Inc. , San Jose, CA 95128, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Merrill","sequence":"additional","affiliation":[{"name":"HypaHealth, HypaHub Inc. , San Jose, CA 95128, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Warrell","sequence":"additional","affiliation":[{"name":"Program in Computational Biology and Bioinformatics, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark B","family":"Gerstein","sequence":"additional","affiliation":[{"name":"Program in Computational Biology and Bioinformatics, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Computer Science, Yale University , New Haven, CT 06520, USA"},{"name":"Department of Statistics & Data Science, Yale University , New Haven, CT 06520, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0564-6105","authenticated-orcid":false,"given":"Hugo Y K","family":"Lam","sequence":"additional","affiliation":[{"name":"HypaHealth, HypaHub Inc. , San Jose, CA 95128, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"2023010805404274700_btac789-B1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.softx.2015.06.001","article-title":"GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers","volume":"1-2","author":"Abraham","year":"2015","journal-title":"SoftwareX"},{"key":"2023010805404274700_btac789-B2","first-page":"Unit7.20","article-title":"Predicting functional effect of human missense mutations using PolyPhen-2","volume":"Chapter 7","author":"Adzhubei","year":"2013","journal-title":"Curr. 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