Article: Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy.
2024 Volume 16, Issue 1, Page(s) 27
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 ... ...
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 strategy proves to have the most relevant results in the top |
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Language | English |
Publishing date | 2024-03-07 |
Publishing country | England |
Document type | Journal Article |
ZDB-ID | 2486539-4 |
ISSN | 1758-2946 |
ISSN | 1758-2946 |
DOI | 10.1186/s13321-024-00816-1 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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