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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 20 % 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: https://bioquimio.udla.edu.ec/tidentification01/ . 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.
摘要：
为了了解化合物的作用机制及其副作用，以及药物发现，预测其可能的蛋白质靶标至关重要。这项研究检查了15个开发的目标中心模型（TCM），采用不同的分子描述和机器学习算法。它们与作为网络工具（WTCM）实现的17个第三方模型进行了对比。在这两组模型中，共识策略被实施为对个体预测的潜在改进.研究结果表明，中医的f1评分值大于0.8。比较这两种方法，最佳中医的真阳性/阴性率达到0.75、0.61、0.25和0.38的值(TPR，TNR）和假阴性/阳性率（FNR，FPR）；表现优于最佳WTCM。此外，共识策略被证明在目标概况的前20%中具有最相关的结果。中医共识达到TPR和FNR值0.98和0；而WTCM达到0.75和0.24。与TCM实现的计算工具及其共识策略在：https://bioquimio。udla.edu.ec/tidentification01/。科学贡献：我们比较和讨论了17种公共复合-目标相互作用预测模型和15种新结构的性能。我们还探索了一种使用共识方法的复合-目标交互优先级策略，我们分析了交互建模中涉及的挑战。
