{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Thafar MA"],"funding":["King Abdullah University of Science and Technology"],"pagination":["4751"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8934358"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(1)"],"pubmed_abstract":["Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to predict continuous values that indicate a drug's ability to bind to a specific target. The regression-based methods provide insight beyond the binary relationship. However, most of these methods require the three-dimensional (3D) structural information of targets which are still not generally available to the targets. Despite this bottleneck, only a few methods address the drug-target binding affinity (DTBA) problem from a non-structure-based approach to avoid the 3D structure limitations. Here we propose Affinity2Vec, as a novel regression-base"],"journal":["Scientific reports"],"pubmed_title":["Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning."],"pmcid":["PMC8934358"],"funding_grant_id":["BAS/1/1059-01-01","URF/1/4098-01-01","FCC/1/1976-20-01","REI/1/4216-01-01","REI/1/4473- 01-01","BAS/1/1624-01-01","REI/1/4437-01-01","FCC/1/1976-26-01","URF/1/3450-01-01"],"pubmed_authors":["Alshahrani M","Essack M","Gojobori T","Gao X","Albaradei S","Thafar MA"],"additional_accession":[]},"is_claimable":false,"name":"Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.","description":"Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to predict continuous values that indicate a drug's ability to bind to a specific target. The regression-based methods provide insight beyond the binary relationship. However, most of these methods require the three-dimensional (3D) structural information of targets which are still not generally available to the targets. Despite this bottleneck, only a few methods address the drug-target binding affinity (DTBA) problem from a non-structure-based approach to avoid the 3D structure limitations. Here we propose Affinity2Vec, as a novel regression-base","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2026-07-14T14:45:37.048Z","creation":"2025-04-04T22:04:12.286Z"},"accession":"S-EPMC8934358","cross_references":{"pubmed":["35306525"],"doi":["10.1038/s41598-022-08787-9"]}}