{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16(1)"],"submitter":["Jimenes-Vargas K"],"pubmed_abstract":["For understanding a chemical compound's mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus"],"journal":["Journal of cheminformatics"],"pagination":["27"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10919000"],"repository":["biostudies-literature"],"pubmed_title":["Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy."],"pmcid":["PMC10919000"],"pubmed_authors":["Perez-Castillo Y","Jimenes-Vargas K","Munteanu CR","Pazos A","Tejera E"],"additional_accession":[]},"is_claimable":false,"name":"Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy.","description":"For understanding a chemical compound's mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-06-12T10:06:19.804Z","creation":"2025-04-04T12:34:52.972Z"},"accession":"S-EPMC10919000","cross_references":{"pubmed":["38449058"],"doi":["10.1186/s13321-024-00816-1"]}}