Deep graph learning in molecular docking: Advances and opportunities
One of the main computational tools for structure-based drug discovery is molecular docking. Due to the natural representation of molecules as graphs (a set of nodes/atoms connected through edges/bonds), Deep Graph Learning has been successfully applied for multiple tasks on this area. This work pre...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Artificial Intelligence in the Life Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667318523000065 |
| _version_ | 1797797074605965312 |
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| author | Norberto Sánchez-Cruz |
| author_facet | Norberto Sánchez-Cruz |
| author_sort | Norberto Sánchez-Cruz |
| collection | DOAJ |
| description | One of the main computational tools for structure-based drug discovery is molecular docking. Due to the natural representation of molecules as graphs (a set of nodes/atoms connected through edges/bonds), Deep Graph Learning has been successfully applied for multiple tasks on this area. This work presents an overview of Deep Graph Learning methods developed within this research field, as well as opportunities for future development. |
| first_indexed | 2024-03-13T03:43:39Z |
| format | Article |
| id | doaj.art-820c760d8e9d4227a7e0f08d090fee62 |
| institution | Directory Open Access Journal |
| issn | 2667-3185 |
| language | English |
| last_indexed | 2024-03-13T03:43:39Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Artificial Intelligence in the Life Sciences |
| spelling | doaj.art-820c760d8e9d4227a7e0f08d090fee622023-06-23T04:45:02ZengElsevierArtificial Intelligence in the Life Sciences2667-31852023-12-013100062Deep graph learning in molecular docking: Advances and opportunitiesNorberto Sánchez-Cruz0Instituto de Química, Unidad Mérida, Universidad Nacional Autónoma de México, Carretera Mérida-Tetiz Km. 4.5, Ucú, Yucatán 97357, Mexico; Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Unidad Mérida, Universidad Nacional Autónoma de México, Sierra Papacál, Mérida, Yucatán 97302, MexicoOne of the main computational tools for structure-based drug discovery is molecular docking. Due to the natural representation of molecules as graphs (a set of nodes/atoms connected through edges/bonds), Deep Graph Learning has been successfully applied for multiple tasks on this area. This work presents an overview of Deep Graph Learning methods developed within this research field, as well as opportunities for future development.http://www.sciencedirect.com/science/article/pii/S2667318523000065Structure-based drug discoveryMolecular dockingMachine learningDeep graph learning |
| spellingShingle | Norberto Sánchez-Cruz Deep graph learning in molecular docking: Advances and opportunities Artificial Intelligence in the Life Sciences Structure-based drug discovery Molecular docking Machine learning Deep graph learning |
| title | Deep graph learning in molecular docking: Advances and opportunities |
| title_full | Deep graph learning in molecular docking: Advances and opportunities |
| title_fullStr | Deep graph learning in molecular docking: Advances and opportunities |
| title_full_unstemmed | Deep graph learning in molecular docking: Advances and opportunities |
| title_short | Deep graph learning in molecular docking: Advances and opportunities |
| title_sort | deep graph learning in molecular docking advances and opportunities |
| topic | Structure-based drug discovery Molecular docking Machine learning Deep graph learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667318523000065 |
| work_keys_str_mv | AT norbertosanchezcruz deepgraphlearninginmoleculardockingadvancesandopportunities |