CAML: Commutative algebra machine learning -- a case study on protein-ligand binding affinity prediction
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915259761033216 |
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| author | Feng, Hongsong Suwayyid, Faisal Zia, Mushal Wee, JunJie Hozumi, Yuta Chen, Chunlong Wei, Guo-Wei |
| author_facet | Feng, Hongsong Suwayyid, Faisal Zia, Mushal Wee, JunJie Hozumi, Yuta Chen, Chunlong Wei, Guo-Wei |
| contents | Recently, Suwayyid and Wei have introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we integrate commutative algebra machine learning (CAML) for the prediction of protein-ligand binding affinities. Specifically, we apply persistent Stanley-Reisner theory, a key concept in combinatorial commutative algebra, to the affinity predictions of protein-ligand binding and metalloprotein-ligand binding. We introduce three new algorithms, i.e., element-specific commutative algebra, category-specific commutative algebra, and commutative algebra on bipartite complexes, to address the complexity of data involved in (metallo) protein-ligand complexes. We show that the proposed CAML outperforms other state-of-the-art methods in (metallo) protein-ligand binding affinity predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18646 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CAML: Commutative algebra machine learning -- a case study on protein-ligand binding affinity prediction Feng, Hongsong Suwayyid, Faisal Zia, Mushal Wee, JunJie Hozumi, Yuta Chen, Chunlong Wei, Guo-Wei Biomolecules Recently, Suwayyid and Wei have introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we integrate commutative algebra machine learning (CAML) for the prediction of protein-ligand binding affinities. Specifically, we apply persistent Stanley-Reisner theory, a key concept in combinatorial commutative algebra, to the affinity predictions of protein-ligand binding and metalloprotein-ligand binding. We introduce three new algorithms, i.e., element-specific commutative algebra, category-specific commutative algebra, and commutative algebra on bipartite complexes, to address the complexity of data involved in (metallo) protein-ligand complexes. We show that the proposed CAML outperforms other state-of-the-art methods in (metallo) protein-ligand binding affinity predictions. |
| title | CAML: Commutative algebra machine learning -- a case study on protein-ligand binding affinity prediction |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2504.18646 |