Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lim, Sangrak | - |
dc.contributor.author | Lee, Yong Oh | - |
dc.contributor.author | Yoon, Juyong | - |
dc.contributor.author | Kim, Young Jun | - |
dc.date.accessioned | 2024-04-16T03:00:37Z | - |
dc.date.available | 2024-04-16T03:00:37Z | - |
dc.date.issued | 2022-03-01 | - |
dc.identifier.issn | 0920-654X | - |
dc.identifier.issn | 1573-4951 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32971 | - |
dc.description.abstract | Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engineering is required for existing methods. In addition, there is a need for a robust model for the sequential combination of pose and affinity prediction due to the probabilistic deviation of the ligand position issue. We propose a pipeline using a bipartite graph neural network and transfer learning trained on a re-docking dataset. We evaluated our model on the released data from drug design data resource grand challenge 4 (D3R GC4). The two target protein data provided by the challenge have different patterns. The model outperformed the best participant by 9% on the BACE target protein from stage 2. Further, our model showed competitive performance on the CatS target protein. | - |
dc.format.extent | 11 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER | - |
dc.title | Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4 | - |
dc.type | Article | - |
dc.publisher.location | 네델란드 | - |
dc.identifier.doi | 10.1007/s10822-022-00448-3 | - |
dc.identifier.scopusid | 2-s2.0-85126762361 | - |
dc.identifier.wosid | 000771387600002 | - |
dc.identifier.bibliographicCitation | JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, v.36, no.3, pp 225 - 235 | - |
dc.citation.title | JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN | - |
dc.citation.volume | 36 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 225 | - |
dc.citation.endPage | 235 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biophysics | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Biochemistry & Molecular Biology | - |
dc.relation.journalWebOfScienceCategory | Biophysics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.subject.keywordPlus | PROTEIN | - |
dc.subject.keywordPlus | MANAGEMENT | - |
dc.subject.keywordPlus | CHEMISTRY | - |
dc.subject.keywordAuthor | Molecular docking | - |
dc.subject.keywordAuthor | Binding affinity | - |
dc.subject.keywordAuthor | D3R-drug design data resource | - |
dc.subject.keywordAuthor | Deep learning | - |
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