<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.
Authors
- Alexandros Patsilinakos (ORCID: https://orcid.org/0000-0001-5961-3328)
- Lars Meuser (ORCID: https://orcid.org/0000-0002-2614-768X)
- Pietro Faccioli (ORCID: https://orcid.org/0000-0002-5546-5054)
Institutions
- Siena Biotech (Italy) (IT)
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano Bicocca (IT)
- University of Milano-Bicocca (IT)
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