Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency.
With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to output the binding strength is a crucial problem th...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1013074 |
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| author | Debby D Wang Yuting Huang |
| author_facet | Debby D Wang Yuting Huang |
| author_sort | Debby D Wang |
| collection | DOAJ |
| description | With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to output the binding strength is a crucial problem that heavily relates to computational drug discovery. Aiming at this problem, we have proposed an efficient scoring framework using deep learning techniques. This framework describes a binding structure by a high-resolution atomic graph, places a focus on the inter-molecular interactions and learns the graph in a rational way. For a protein-ligand binding complex, the generated atomic graph reserves key information of the atoms (as graph nodes), and focuses on inter-molecular interactions (as graph edges) that can be identified by introducing multiple distance ranges to the atom pairs within the binding area. To provide more confidence in the predicted binding strengths, we have interpreted the deep learning model from the model level and in a post-hoc analysis. The proposed learning framework has been demonstrated to have competitive performance in scoring and screening tasks, which will prospectively promote the development of related fields further. |
| format | Article |
| id | doaj-art-0f2458a3f49a40a8a73bf5e2726a0b87 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-0f2458a3f49a40a8a73bf5e2726a0b872025-08-20T02:26:11ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-05-01215e101307410.1371/journal.pcbi.1013074Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency.Debby D WangYuting HuangWith a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to output the binding strength is a crucial problem that heavily relates to computational drug discovery. Aiming at this problem, we have proposed an efficient scoring framework using deep learning techniques. This framework describes a binding structure by a high-resolution atomic graph, places a focus on the inter-molecular interactions and learns the graph in a rational way. For a protein-ligand binding complex, the generated atomic graph reserves key information of the atoms (as graph nodes), and focuses on inter-molecular interactions (as graph edges) that can be identified by introducing multiple distance ranges to the atom pairs within the binding area. To provide more confidence in the predicted binding strengths, we have interpreted the deep learning model from the model level and in a post-hoc analysis. The proposed learning framework has been demonstrated to have competitive performance in scoring and screening tasks, which will prospectively promote the development of related fields further.https://doi.org/10.1371/journal.pcbi.1013074 |
| spellingShingle | Debby D Wang Yuting Huang Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency. PLoS Computational Biology |
| title | Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency. |
| title_full | Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency. |
| title_fullStr | Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency. |
| title_full_unstemmed | Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency. |
| title_short | Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency. |
| title_sort | scoring protein ligand binding structures through learning atomic graphs with inter molecular adjacency |
| url | https://doi.org/10.1371/journal.pcbi.1013074 |
| work_keys_str_mv | AT debbydwang scoringproteinligandbindingstructuresthroughlearningatomicgraphswithintermolecularadjacency AT yutinghuang scoringproteinligandbindingstructuresthroughlearningatomicgraphswithintermolecularadjacency |