Lau B, Emani PS, Chapman J, Yao L, Lam T, Merrill P, Warrell J, Gerstein MB, Lam HYK. Insights from incorporating quantum computing into drug design workflows.
Bioinformatics 2023;
39:6881079. [PMID:
36477833 PMCID:
PMC9825754 DOI:
10.1093/bioinformatics/btac789]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/14/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022] Open
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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