QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2509.05051v1
• Date: Fri, 05 Sep 2025 12:31:58 GMT
• Title: QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning
• Authors: Aaron Mark Thomas, Yu-Cheng Chen, Hubert Okadome Valencia, Sharu Theresa Jose, Ronin Wu,
• Abstract summary: This work presents a novel quantum circuit Born machine (QCBM)-enabled Generative Adrialversa Network (GAN), called QCA-MolGAN, for generating drug-like molecules.<n>QCBM serves as a learnable prior distribution, which is associatively trained to define a latent space aligning with high-level features captured by the GANs discriminator.<n>We integrate a novel multi-agent reinforcement learning network to guide molecular generation with desired targeted properties.
• Score: 2.2463678997251604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Navigating the vast chemical space of molecular structures to design novel drug molecules with desired target properties remains a central challenge in drug discovery. Recent advances in generative models offer promising solutions. This work presents a novel quantum circuit Born machine (QCBM)-enabled Generative Adversarial Network (GAN), called QCA-MolGAN, for generating drug-like molecules. The QCBM serves as a learnable prior distribution, which is associatively trained to define a latent space aligning with high-level features captured by the GANs discriminator. Additionally, we integrate a novel multi-agent reinforcement learning network to guide molecular generation with desired targeted properties, optimising key metrics such as quantitative estimate of drug-likeness (QED), octanol-water partition coefficient (LogP) and synthetic accessibility (SA) scores in conjunction with one another. Experimental results demonstrate that our approach enhances the property alignment of generated molecules with the multi-agent reinforcement learning agents effectively balancing chemical properties.

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