Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
Abstract
Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to improve their properties according to drug-relevant oracle functions (i.e., objective) while ensuring synthetic feasibility. However, existing methods are mostly based on random search. To address this issue, in this paper, we introduce a novel approach using the reinforcement learning method with quantum-inspired simulated annealing policy neural network to navigate the vast discrete space of chemical structures intelligently. Specifically, we employ a deterministic REINFORCE algorithm using policy neural networks to output transitional probability to guide state transitions and local search using genetic algorithm to refine solutions to a local optimum within each iteration. Our methods are evaluated with the Practical Molecular Optimization (PMO) benchmark framework with a 10K query budget. We further showcase the competitive performance of our method by comparing it against the state-of-the-art genetic algorithms-based method.
Cite
@article{arxiv.2409.09183,
title = {Quantum-inspired Reinforcement Learning for Synthesizable Drug Design},
author = {Dannong Wang and Jintai Chen and Yingzhou Lu and Minjie Shen and Lulu Chen and Zhiding Liang and Tianfan Fu and Xiao-Yang Liu},
journal= {arXiv preprint arXiv:2409.09183},
year = {2026}
}