Can Molecular Evolution Mechanism Enhance Molecular Representation?

Authors

  • Kun Li School of Computer Science, Wuhan University, Wuhan, China
  • Longtao Hu School of Computer Science, Wuhan University, Wuhan, China
  • Jiameng Chen School of Computer Science, Wuhan University, Wuhan, China
  • Hongzhi Zhang School of Computer Science, Wuhan University, Wuhan, China
  • Yida Xiong School of Computer Science, Wuhan University, Wuhan, China
  • Xiantao Cai School of Computer Science, Wuhan University, Wuhan, China
  • Wenbin Hu Shenzhen Research Institute, Wuhan University, Shenzhen, China School of Computer Science, Wuhan University, Wuhan, China
  • Jia Wu Department of Computing, Macquarie University, Sydney, Australia

DOI:

https://doi.org/10.1609/aaai.v40i18.38534

Abstract

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 by approximately 33% on both the QM7 and QM9 datasets.

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Published

2026-03-14

How to Cite

Li, K., Hu, L., Chen, J., Zhang, H., Xiong, Y., Cai, X., … Wu, J. (2026). Can Molecular Evolution Mechanism Enhance Molecular Representation?. Proceedings of the AAAI Conference on Artificial Intelligence, 40(18), 15108–15116. https://doi.org/10.1609/aaai.v40i18.38534

Issue

Section

AAAI Technical Track on Data Mining & Knowledge Management II