CAML: Commutative Algebra Machine Learning─A Case Study on Protein–Ligand Binding Affinity Prediction

Recently, Suwayyid and Wei introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we propose 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 present three new algorithms, i.e., element-specific commutative algebra, category-specific commutative algebra, and commutative algebra on bipartite complexes, to tackle 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, indicating the great potential of commutative algebra learning.

Authors

Institutions

Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-06-16
DOI
https://doi.org/10.1021/acs.jcim.5c00940
Citations
6
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
9.03

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CAML: Commutative Algebra Machine Learning─A Case Study on Protein–Ligand Binding Affinity Prediction

Hongsong Feng, Chun‐Long Chen, Mushal Zia, Guo‐Wei Wei et al.
6 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
9.03
article

CAML: Commutative Algebra Machine Learning─A Case Study on Protein–Ligand Binding Affinity Prediction

Hongsong Feng, Chun‐Long Chen, Mushal Zia, Guo‐Wei Wei, JunJie Wee, Yuta Hozumi, Faisal Suwayyid
article en
6 citations

Abstract

Recently, Suwayyid and Wei introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we propose 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 present three new algorithms, i.e., element-specific commutative algebra, category-specific commutative algebra, and commutative algebra on bipartite complexes, to tackle 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, indicating the great potential of commutative algebra learning.

Journal of Chemical Information and ModelingVol. 65(13)
University of North Carolina at Charlotte (US), King Fahd University of Petroleum and Minerals (SA), Pacific Northwest National Laboratory (US), Physical Sciences (United States) (US), Toronto Metropolitan University (CA), Michigan State University (US)
National Science Foundation, Bristol-Myers Squibb, Michigan State University Foundation, King Fahd University of Petroleum and Minerals, Defense Threat Reduction Agency, NIH Clinical Center, Pacific Northwest National Laboratory
Openalex Percentile: Top 2%
Computational Drug Discovery Methods
9.03
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.