Hybrid quantum-classical machine learning for generative chemistry and drug design

TL;DR

This paper demonstrates a hybrid quantum-classical approach using a compact variational autoencoder with a quantum annealer to generate novel drug-like molecules, showing the potential of quantum computing in drug discovery.

Contribution

It introduces a small-scale hybrid quantum-classical model for molecular generation, compatible with current quantum annealers, advancing quantum applications in chemistry.

Findings

Generated 2331 novel chemical structures with drug-like properties

Model successfully trained on a subset of ChEMBL dataset

Proves feasibility of using existing quantum devices for drug discovery

Abstract

Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The presented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications.