用量子电路生成分子，通过多智能体强化学习精准调控药物属性。
QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning
- 结合量子电路与对抗网络，构建可学习的分子潜在空间。
- 多智能体强化学习协同优化QED、LogP和SA得分，提升药物属性匹配度。
- 适合需要高效生成高属性契合分子的药物研发人员使用。
在浩瀚的分子结构化学空间中设计具备特定目标性质的新型药物分子，仍是药物发现的核心挑战。生成模型的最新进展为此提供了有前景的解决方案。本文提出一种基于量子电路波恩机（QCBM）的生成对抗网络（GAN），命名为QCA-MolGAN，用于生成类药物分子。其中，QCBM作为可学习的先验分布，与生成对抗网络的判别器共同关联，定义出与高级特征对齐的潜在空间。此外，我们引入一种新型多智能体强化学习网络，引导分子生成以满足特定目标性质，同时优化定量药物相似性（QED）、辛醇-水分配系数（LogP）和合成可及性（SA）等关键指标。实验结果表明，该方法能有效提升生成分子的性质匹配度，并通过多智能体强化学习实现化学性质的均衡优化。
原文摘要 · Abstract (English)
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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