CoCoGraph可高效生成化学合法分子，且更接近真实分子分布。
A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules
- 通过约束与协作机制，确保生成分子的化学合法性。
- 在标准测试中优于现有方法，参数量少10倍。
- 适合药物发现与分子设计领域研究人员使用。
开发新分子化合物对应对健康与环境可持续性等挑战至关重要，但分子空间过于庞大，难以探索。本文提出CoCoGraph，一种协同约束的图扩散模型，可生成保证化学合法性的分子。得益于模型内置约束与协作机制，CoCoGraph在标准基准上表现优于当前最优方法，参数量减少达一个数量级。36种化学性质分析表明，其生成分子的分布更贴近真实分子。利用模型高效性，构建了包含820万合成分子的数据库，并邀请有机化学专家进行类图灵测试，评估生成分子的合理性及模型潜在偏差与局限。
原文摘要 · Abstract (English)
Developing new molecular compounds is crucial to address pressing challenges, from health to environmental sustainability. However, exploring the molecular space to discover new molecules is difficult due to the vastness of the space. Here we introduce CoCoGraph, a collaborative and constrained graph diffusion model capable of generating molecules that are guaranteed to be chemically valid. Thanks to the constraints built into the model and to the collaborative mechanism, CoCoGraph outperforms state-of-the-art approaches on standard benchmarks while requiring up to an order of magnitude fewer parameters. Analysis of 36 chemical properties also demonstrates that CoCoGraph generates molecules with distributions more closely matching real molecules than current models. Leveraging the model's efficiency, we created a database of 8.2M million synthetically generated molecules and conducted a Turing-like test with organic chemistry experts to further assess the plausibility of the generated molecules, and potential biases and limitations of CoCoGraph.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。