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Recent advances in machine learning (ML)\u2013based scoring functions have improved these predictions, yet challenges remain in modeling complex molecular interactions. This study introduces the AGL-EAT-Score, a scoring function that integrates extended atom-type multiscale weighted colored subgraphs with algebraic graph theory. This approach leverages the eigenvalues and eigenvectors of graph Laplacian and adjacency matrices to capture high-level details of specific atom pairwise interactions. Evaluated against benchmark datasets such as CASF-2016, CASF-2013, and the Cathepsin S dataset, the AGL-EAT-Score demonstrates notable accuracy, outperforming existing traditional and ML-based methods. The model\u2019s strength lies in its comprehensive similarity analysis, examining protein sequence, ligand structure, and binding site similarities, thus ensuring minimal bias and over-representation in the training sets. The use of extended atom types in graph coloring enhances the model\u2019s capability to capture the intricacies of protein-ligand interactions. The AGL-EAT-Score marks a significant advancement in drug design, offering a tool that could potentially refine and accelerate the drug discovery process.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Scientific Contribution<\/jats:bold>\n                  <\/jats:p>\n                  <jats:p>The AGL-EAT-Score presents an algebraic graph-based framework that predicts ligand-receptor binding affinity by constructing multiscale weighted colored subgraphs from the 3D structure of protein-ligand complexes. It improves prediction accuracy by modeling interactions between extended atom types, addressing challenges like dataset bias and over-representation. Benchmark evaluations demonstrate that AGL-EAT-Score outperforms existing methods, offering a robust and systematic tool for structure-based drug design.<\/jats:p>","DOI":"10.1186\/s13321-025-00955-z","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T08:24:55Z","timestamp":1737534295000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["The algebraic extended atom-type graph-based model for precise ligand\u2013receptor binding affinity prediction"],"prefix":"10.1186","volume":"17","author":[{"given":"Farjana Tasnim","family":"Mukta","sequence":"first","affiliation":[]},{"given":"Md Masud","family":"Rana","sequence":"additional","affiliation":[]},{"given":"Avery","family":"Meyer","sequence":"additional","affiliation":[]},{"given":"Sally","family":"Ellingson","sequence":"additional","affiliation":[]},{"given":"Duc D.","family":"Nguyen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"issue":"1","key":"955_CR1","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1093\/bib\/bbab476","volume":"23","author":"A Dhakal","year":"2022","unstructured":"Dhakal A, McKay C, Tanner JJ, Cheng J (2022) Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions. 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