Hybrid quantum-classical machine learning for generative chemistry and drug design
This work addresses the problem of accelerating drug discovery for pharmaceutical research by demonstrating the feasibility of using quantum annealers, though it is incremental as it builds on existing quantum-classical hybrid approaches.
The paper tackled the challenge of exploring the vast structural space of drug-like molecules by developing a hybrid quantum-classical machine learning model, a compact discrete variational autoencoder with a Restricted Boltzmann Machine, which generated 2331 novel chemical structures with properties typical for biologically active compounds from the ChEMBL dataset.
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.