Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

TL;DR

This paper introduces a quantum annealing-enhanced generative model for molecular design that produces higher quality, drug-like compounds, surpassing classical models and even training data in drug-likeness.

Contribution

It presents a novel framework integrating quantum annealing with neural hash functions for improved molecular generation in drug discovery.

Findings

Generated compounds showed higher validity and drug-likeness than classical models.

Quantum annealing models exceeded training data in drug-likeness features.

The approach enhances feature space sampling and extraction in molecular design.

Abstract

Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. However, one major issue in molecular generative models is their lower frequency of drug-like compounds. To resolve this problem, we developed a novel framework for optimization of deep generative models integrated with a D-Wave quantum annealing computer, where our Neural Hash Function (NHF) presented herein is used both as the regularization and binarization schemes simultaneously, of which the latter is for transformation between continuous and discrete signals of the classical and quantum neural networks, respectively, in the error evaluation (i.e., objective) function. The compounds generated via the quantum-annealing generative models exhibited higher quality in both validity and drug-likeness than those generated via the fully-classical models, and was further indicated to exceed even the training data in terms of drug-likeness features, without any restraints and conditions to deliberately induce such an optimization. These results indicated an advantage of quantum annealing to aim at a stochastic generator integrated with our novel neural network architectures, for the extended performance of feature space sampling and extraction of characteristic features in drug design.