DMol高效生成分子，保持化学结构特征且速度翻倍。
DMol: A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platform
- 用新目标函数和分步节点噪声调度提升生成效率。
- 比DiGress有效率高1.5%，扩散步数减少10倍，耗时减半。
- 支持环结构压缩，适合药物分子设计与快速生成。
我们提出一种新的图扩散模型DMol，用于小分子生成。在所有基准数据集上，其有效性比当前最先进的DiGress模型高出约1.5%，同时扩散步数减少至少10倍，运行时间缩短至约一半。性能提升源于目标函数的精心设计及一种图噪声调度策略：每步仅更新分子图中大小可变的一组节点。该方法还可与类似枝树（junction-tree）的图表示结合，将一组关键环结构压缩为超节点。不同于传统枝树方法依赖变分自编码器（VAE）并需复杂重构步骤，压缩版DMol直接在压缩后的图上进行扩散，仅将频繁出现的碳环结构选入超节点，从而实现更简洁的采样生成。该压缩版本相比通用版DMol，有效性进一步提升约2%，新颖性更高，且因图规模减小而运行更快。
原文摘要 · Abstract (English)
We introduce a new graph diffusion model for small molecule generation, DMol, which outperforms the state-of-the-art DiGress model in terms of validity by roughly 1.5% across all benchmarking datasets while reducing the number of diffusion steps by at least 10-fold, and the running time to roughly one half. The performance improvements are a result of a careful change in the objective function and a graph noise scheduling approach which, at each diffusion step, allows one to only change a subset of nodes of varying size in the molecule graph. Another relevant property of the method is that it can be easily combined with junction-tree-like graph representations that arise by compressing a collection of relevant ring structures into supernodes. Unlike classical junction-tree techniques that involve VAEs and require complicated reconstruction steps, compressed DMol directly performs graph diffusion on a graph that compresses only a carefully selected set of frequent carbon rings into supernodes, which results in straightforward sample generation. This compressed DMol method offers additional validity improvements over generic DMol of roughly 2%, increases the novelty of the method, and further improves the running time due to reductions in the graph size.
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