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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 strategy proves to have the most relevant results in the top <jats:inline-formula><jats:alternatives><jats:tex-math>$$20\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>20<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/bioquimio.udla.edu.ec\/tidentification01\/\">https:\/\/bioquimio.udla.edu.ec\/tidentification01\/<\/jats:ext-link>. Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling.<\/jats:p>\n                <jats:p><jats:bold>Graphical Abstract<\/jats:bold><\/jats:p>","DOI":"10.1186\/s13321-024-00816-1","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:42:21Z","timestamp":1709772141000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy"],"prefix":"10.1186","volume":"16","author":[{"given":"Karina","family":"Jimenes-Vargas","sequence":"first","affiliation":[]},{"given":"Alejandro","family":"Pazos","sequence":"additional","affiliation":[]},{"given":"Cristian R.","family":"Munteanu","sequence":"additional","affiliation":[]},{"given":"Yunierkis","family":"Perez-Castillo","sequence":"additional","affiliation":[]},{"given":"Eduardo","family":"Tejera","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"issue":"1","key":"816_CR1","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1021\/cb100294v","volume":"6","author":"B Lomenick","year":"2011","unstructured":"Lomenick B, Olsen RW, Huang J (2011) Identification of direct protein targets of small molecules. 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