分子进化机制能否增强分子表示？
Can Molecular Evolution Mechanism Enhance Molecular Representation?
AI总结：
本文提出分子进化网络（MEvoN），通过模拟分子进化路径并建模原子级变化来增强分子表示，显著提升了传统端到端算法在分子性质预测任务上的性能。
摘要：
分子进化是在化学空间中模拟分子自然进化以探索潜在分子结构和性质的过程。相似分子间的关系常通过添加、删除和修改原子及化学键等变换来描述，反映特定的进化路径。现有分子表示方法主要侧重于直接从分子中挖掘原子级结构和化学键等数据，往往忽略了其进化历史。因此，我们旨在探索通过模拟进化过程来增强分子表示的可能性。我们提取并分析进化路径中的变化，并探索将其与现有分子表示相结合。因此，本文提出用于分子表示的分子进化网络（MEvoN）。首先，我们利用具有少量原子的分子构建MEvoN，并利用相似性计算生成进化路径。然后，通过对原子级变化进行建模，MEvoN揭示了它们对分子性质的影响。实验结果表明，基于MEvoN的分子性质预测方法显著提升了传统端到端算法在几个分子数据集上的性能。代码可在 https://anonymous.4open.science/r/MEvoN-7416/ 获取。
英文摘要：
Molecular evolution is the process of simulating the natural evolution of molecules in chemical space to explore potential molecular structures and properties. The relationships between similar molecules are often described through transformations such as adding, deleting, and modifying atoms and chemical bonds, reflecting specific evolutionary paths. Existing molecular representation methods mainly focus on mining data, such as atomic-level structures and chemical bonds directly from the molecules, often overlooking their evolutionary history. Consequently, we aim to explore the possibility of enhancing molecular representations by simulating the evolutionary process. We extract and analyze the changes in the evolutionary pathway and explore combining it with existing molecular representations. Therefore, this paper proposes the molecular evolutionary network (MEvoN) for molecular representations. First, we construct the MEvoN using molecules with a small number of atoms and generate evolutionary paths utilizing similarity calculations. Then, by modeling the atomic-level changes, MEvoN reveals their impact on molecular properties. Experimental results show that the MEvoN-based molecular property prediction method significantly improves the performance of traditional end-to-end algorithms on several molecular datasets. The code is available at https://anonymous.4open.science/r/MEvoN-7416/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
