The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction
Abstract Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)–based scoring functions have improved these predictions, yet challenges remain in modeling...
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BMC
2025-01-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-00955-z |
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| author | Farjana Tasnim Mukta Md Masud Rana Avery Meyer Sally Ellingson Duc D. Nguyen |
| author_facet | Farjana Tasnim Mukta Md Masud Rana Avery Meyer Sally Ellingson Duc D. Nguyen |
| author_sort | Farjana Tasnim Mukta |
| collection | DOAJ |
| description | Abstract Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)–based 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’s 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’s 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. Scientific Contribution 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. |
| format | Article |
| id | doaj-art-5c44853ede4f47178dcda5315b5a5ecc |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | Journal of Cheminformatics |
| spelling | doaj-art-5c44853ede4f47178dcda5315b5a5ecc2025-01-26T12:50:04ZengBMCJournal of Cheminformatics1758-29462025-01-0117111510.1186/s13321-025-00955-zThe algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity predictionFarjana Tasnim Mukta0Md Masud Rana1Avery Meyer2Sally Ellingson3Duc D. Nguyen4Department of Mathematics, University of KentuckyDepartment of Mathematics, Kennesaw State UniversityDepartment of Mathematics, University of KentuckyDivision of Biomedical Informatics, College of Medicine, University of KentuckyDepartment of Mathematics, University of TennesseeAbstract Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)–based 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’s 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’s 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. Scientific Contribution 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.https://doi.org/10.1186/s13321-025-00955-zAlgebraic graph learningExtended atom typeSimilarity computationNon-redundant training setsProtein-ligand interactionsBinding affinity predictions |
| spellingShingle | Farjana Tasnim Mukta Md Masud Rana Avery Meyer Sally Ellingson Duc D. Nguyen The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction Journal of Cheminformatics Algebraic graph learning Extended atom type Similarity computation Non-redundant training sets Protein-ligand interactions Binding affinity predictions |
| title | The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction |
| title_full | The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction |
| title_fullStr | The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction |
| title_full_unstemmed | The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction |
| title_short | The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction |
| title_sort | algebraic extended atom type graph based model for precise ligand receptor binding affinity prediction |
| topic | Algebraic graph learning Extended atom type Similarity computation Non-redundant training sets Protein-ligand interactions Binding affinity predictions |
| url | https://doi.org/10.1186/s13321-025-00955-z |
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