<i>De Novo</i> Design of Protein-Binding Peptides by Quantum Computing

Physics-based approaches to <i>de novo</i> drug design involve the simultaneous exploration of vast chemical and conformational spaces. The rapid development of quantum computing technologies offers a promising perspective to efficiently tackle this challenge. In this work, we focus on peptide design and present a multiscale framework that combines classical and quantum computing to optimize amino acid sequences and predict binding poses at atomic resolution. We illustrate our scheme by designing binders for several protein targets, and we contrast the performance of the D-Wave quantum annealer with that of an industry-grade classical optimizer. To assess our results, we compare the designed sequences and the corresponding poses with those available in a data set of experimentally characterized peptide binders.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2025-09-29
DOI
https://doi.org/10.1021/acs.jctc.5c00768
Citations
2
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
1.61
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article

De Novo Design of Protein-Binding Peptides by Quantum Computing

Alexandros Patsilinakos, Lars Meuser, Pietro Faccioli
2 citations
Journal of Chemical Theory and Computation
Machine Learning in Bioinformatics
1.61
article

De Novo Design of Protein-Binding Peptides by Quantum Computing

Alexandros Patsilinakos, Lars Meuser, Pietro Faccioli
article en
2 citations

Abstract

Physics-based approaches to <i>de novo</i> drug design involve the simultaneous exploration of vast chemical and conformational spaces. The rapid development of quantum computing technologies offers a promising perspective to efficiently tackle this challenge. In this work, we focus on peptide design and present a multiscale framework that combines classical and quantum computing to optimize amino acid sequences and predict binding poses at atomic resolution. We illustrate our scheme by designing binders for several protein targets, and we contrast the performance of the D-Wave quantum annealer with that of an industry-grade classical optimizer. To assess our results, we compare the designed sequences and the corresponding poses with those available in a data set of experimentally characterized peptide binders.

Journal of Chemical Theory and ComputationVol. 21(19)
Siena Biotech (Italy) (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Milano Bicocca (IT), University of Milano-Bicocca (IT)
Openalex Percentile: Top 15%
Machine Learning in Bioinformatics
1.61
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