De Novo Design of Protein-Binding Peptides by Quantum Computing

Abstract

In silico de novo design can drastically cut the costs and time of drug development. In particular, a key advantage of bottom-up physics-based approaches is their independence from training datasets, unlike generative models. However, they require the simultaneous exploration of chemical and conformational space. In this study, we address this formidable challenge leveraging quantum annealers. Focusing on peptide de novo design, we introduce a multi-scale framework that integrates classical and quantum computing for atomically resolved predictions. We assess this scheme by designing binders for several protein targets. The D-Wave quantum annealer rapidly generates a chemically diverse set of binders with primary structures and binding poses that correlate well with experiments. These results demonstrate that, even in their current early stages, quantum technologies can already empower physics-based drug design.

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