通过模拟分子进化路径，提升分子表示能力。
Can Molecular Evolution Mechanism Enhance Molecular Representation?
- 构建分子进化网络，捕捉原子级变化对性质的影响
- 在多个数据集上显著优于传统端到端算法
- 适合研究分子设计与性质预测的学者参考
分子进化是模拟化学空间中分子自然演化的过程，以探索潜在分子结构与性质。相似分子间的关联常通过原子或化学键的增删改等变换描述，体现特定进化路径。现有分子表示方法多聚焦于原子级结构和化学键的直接挖掘，常忽视其进化历史。为此，本文探索通过模拟进化过程增强分子表示的可行性。提出分子进化网络（MEvoN），先用小分子构建进化路径，再基于相似性计算生成演化轨迹；通过建模原子级变化，揭示其对分子性质的影响。实验表明，基于MEvoN的性质预测方法在多个分子数据集上显著优于传统端到端算法。代码已公开于 https://anonymous.4open.science/r/MEvoN-7416/。
原文摘要 · Abstract (English)
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/.
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