Designing microplastic-binding peptides with a variational quantum circuit-based hybrid quantum-classical approach.

ABSTRACT: De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no known peptide binders for many plastics-a gap that can be filled with de novo design. Current computational methods for peptide design exhibit limitations in sampling and scaling that could be addressed with quantum computing. Hybrid quantum-classical methods can leverage complementary strengths of near-term quantum algorithms and classical techniques for complex tasks like peptide design. This work introduces a hybrid quantum-classical generative framework for designing plastic-binding peptides combining variational quantum circuits with a variational autoencoder network. We demonstrate the framework's effecti

SUBMITTER: Vendrell RC 

PROVIDER: S-EPMC11654670 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature