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.

中文翻译：

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使用多种人工智能算法预测复合目标相互作用，并与基于共识的策略进行比较

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。本研究检查了 15 个采用不同分子描述和机器学习算法开发的以目标为中心的模型 (TCM)。它们与 17 个作为网络工具实现的第三方模型 (WTCM) 进行了对比。在两组模型中，都实施了共识策略作为对个人预测的潜在改进。研究结果表明，中医的 f1 分数值大于 0.8。比较这两种方法，最好的 TCM 的真阳性/阴性率（TPR、TNR）和假阴性/阳性率（FNR、FPR）的值分别为 0.75、0.61、0.25 和 0.38；超越最好的 WTCM。此外，事实证明，共识策略在目标配置文件的前 $$20\%$$ 中具有最相关的结果。TCM共识达到TPR和FNR值分别为0.98和0；而在 WTCM 上则达到 0.75 和 0.24 的值。与 TCM 一起实施的计算工具及其共识策略位于：https://bioquimio.udla.edu.ec/tidentification01/。科学贡献：我们比较和讨论了 17 个公共复合目标相互作用预测模型和 15 个新结构的性能。我们还使用共识方法探索了复合目标交互优先策略，并分析了交互建模中涉及的挑战。