Can Molecular Evolution Mechanism Enhance Molecular Representation?
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929687948689408 |
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| author | Li, Kun Hu, Longtao Cai, Xiantao Wu, Jia Hu, Wenbin |
| author_facet | Li, Kun Hu, Longtao Cai, Xiantao Wu, Jia Hu, Wenbin |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can Molecular Evolution Mechanism Enhance Molecular Representation? Li, Kun Hu, Longtao Cai, Xiantao Wu, Jia Hu, Wenbin Biomolecules Machine Learning Neural and Evolutionary Computing 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/. |
| title | Can Molecular Evolution Mechanism Enhance Molecular Representation? |
| topic | Biomolecules Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2501.15799 |