用量子生成分子片段，实现高效可解释的分子设计。
MolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular Generation
- 分模块设计：量子生成片段+经典条件器+化合价感知聚合器
- 生成100%有效分子，99.75%新颖性，多样性达0.905
- 适合需要可解释性与拓扑控制的药物分子生成场景
分子生成模型需兼顾有效性、多样性和属性控制，但现有方法常在三者间权衡。我们提出MolPaQ，一种模块化量子-经典生成框架，通过量子生成的潜在片段拼装分子。基于QM9预训练的β-VAE学习化学对齐的潜在流形；简化条件器将分子描述符映射至该空间；参数高效的量子片段生成器产生纠缠节点嵌入，由化合价感知聚合器重建为有效分子图。结合潜在判别器与化学引导奖励进行对抗微调，实现100% RDKit有效性、99.75%新颖性及0.905多样性。除整体指标外，预训练量子生成器经条件器引导后，平均QED提升约2.3%，芳香环出现率提高约10–12%，凸显其作为紧凑拓扑调控算子的作用。
原文摘要 · Abstract (English)
Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically trade off among these objectives. We present MOLPAQ, a modular quantum-classical generator that assembles molecules from quantum-generated latent patches. A \b{eta}-VAE pretrained on QM9 learns a chemically aligned latent manifold; a reduced conditioner maps molecular descriptors into this space; and a parameter-efficient quantum patch generator produces entangled node embeddings that a valence-aware aggregator reconstructs into valid molecular graphs. Adversarial fine-tuning with a latent critic and chemistry-shaped reward yields 100\% RDKit validity, 99.75\% novelty, and 0.905 diversity. Beyond aggregate metrics, the pretrained quantum generator, steered by the conditioner, improves mean QED by approx. 2.3\% and increases aromatic motif incidence by approx. 10-12\% relative to a parameter-matched classical generator, highlighting its role as a compact topology-shaping operator.
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