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

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.

8 pages. 3 figures, 1 table

References in corpus (3)

• Quantum algorithms for quantum chemistry and quantum materials science
• Experimental Quantum Generative Adversarial Networks for Image Generation
• Training and Classification using a Restricted Boltzmann Machine on the D-Wave 2000Q

Cited by in corpus (7)

• NISQ Computers: A Path to Quantum Supremacy
• A Hybrid Quantum Computing Pipeline for Real World Drug Discovery
• Comprehensive characterization of three-qubit Grover search algorithm on IBM's 127-qubit superconducting quantum computers
• Hybrid quantum cycle generative adversarial network for small molecule generation
• Information processing at the speed of light
• When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
• Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor