提出首个可组合的分子图生成模型，实现多条件精准调控。
Composable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation
- 基于分数匹配构建离散图的可组合分数模型
- 多条件控制下平均可控性提升15.3%且保持高生成质量
- 适合需要多属性协同优化的药物分子设计场景
可控分子图生成对材料与药物发现至关重要，要求生成分子满足多种性质约束。尽管图扩散模型在生成质量上取得进展，但在多条件设置下的有效性仍受限于联合条件依赖或连续松弛带来的保真度损失。为此，我们提出首个基于分数匹配的可组合图扩散模型（CSGD），通过引入具体分数将分数匹配扩展至离散图，实现条件引导的灵活、严谨操作。在此基础上，我们提出两种技术：可组合引导（CoG），可在采样过程中对任意条件子集进行细粒度控制；概率校准（PC），通过调整估计转移概率缓解训练-测试不匹配问题。在四个分子数据集上的实验表明，CSGD达到当前最优性能，相比先前方法平均可控性提升15.3%，同时保持高有效性和分布保真度。研究结果凸显了基于分数建模在离散图生成中的实际优势及其在多属性分子设计中的灵活性。
原文摘要 · Abstract (English)
Controllable molecular graph generation is essential for material and drug discovery, where generated molecules must satisfy diverse property constraints. While recent advances in graph diffusion models have improved generation quality, their effectiveness in multi-conditional settings remains limited due to reliance on joint conditioning or continuous relaxations that compromise fidelity. To address these limitations, we propose Composable Score-based Graph Diffusion model (CSGD), the first model that extends score matching to discrete graphs via concrete scores, enabling flexible and principled manipulation of conditional guidance. Building on this foundation, we introduce two score-based techniques: Composable Guidance (CoG), which allows fine-grained control over arbitrary subsets of conditions during sampling, and Probability Calibration (PC), which adjusts estimated transition probabilities to mitigate train-test mismatches. Empirical results on four molecular datasets show that CSGD achieves state-of-the-art performance, with a 15.3% average improvement in controllability over prior methods, while maintaining high validity and distributional fidelity. Our findings highlight the practical advantages of score-based modeling for discrete graph generation and its capacity for flexible, multi-property molecular design.
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