biostudies-literatureUnknown16(1)Jimenes-Vargas KFor 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 consensusJournal of cheminformatics27https://www.ebi.ac.uk/biostudies/studies/S-EPMC10919000biostudies-literaturePrediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy.PMC10919000Perez-Castillo YJimenes-Vargas KMunteanu CRPazos ATejera EfalsePrediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy.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 consensus2024-01-01T00:00:00Z2024 Mar2026-06-12T10:06:19.804Z2025-04-04T12:34:52.972ZS-EPMC109190003844905810.1186/s13321-024-00816-1