QuantumBound – Interactive protein generation with one-shot learning and hybrid quantum neural networks

Abstract

This paper presents a new approach for protein generation based on one-shot learning and hybrid quantum neural networks. Given a single protein complex, the system learns how to predict the remaining unknown properties, without resorting to autoregression, from the physicochemical properties of the receptor and a prior on the physicochemical properties of the ligand. In contrast with other approaches, QuantumBound learns from a single instance, not from a large dataset, as is common in deep learning. By knowing half of the properties of the ligand, the system can predict the remaining half with an average relative error of 1.43% for a dataset consisting of one hundred and twenty Covid-19 spikes complexes. To the best of our knowledge, this is the first time that one-shot learning and hybrid quantum computing have been applied to protein generation.

Keywords

• Quantum neural network
• Parallel learning
• Deep neural network
• Hybrid quantum neural network
• Protein–protein interaction
• Ligand-receptor