Protein kinases form a consistent class of promising drug targets, and several efforts have been made to predict the activities of small molecules against a representative part of the kinome. This study continues our previous work (Bora, A.; Avram, S.; Ciucanu, I.; Raica, M.; Avram, S. Predictive Models for Fast and Effective Profiling of Kinase Inhibitors. J. Chem. Inf. Model. 2016, 56, 895−905; www.chembioinf.ro) aiming to build and measure the performance of ligand-based kinase inhibitor prediction models. Here we analyzed kinase–inhibitor pairs with multiple activity points extracted from the ChEMBL database and identified the main sources of inconsistency. Our results indicate that lower IC₅₀ values are usually less affected by errors and reflect more accurately the structure–activity relationship of the molecules against the target, ideally for quantitative structure–activity relationship studies. Further, we modeled the activities of 104 kinases using unbiased target-specific activity points. The performance of predictors built on extended connectivity fingerprints (ECFP4) and two-dimensional pharmacophore fingerprints (PFPs) are compared by means of tolerance intervals (TIs) (95%/95%) in virtual screening (VS) and classification tasks using external random (RandSets) and diversity-based (DivSets) test sets. We found that the two encodings perform superior to each other on different kinases in VS and that PFP models perform consistently better in classifying actives (higher sensitivity). Next, we combined the two encodings into a single one (PFPECFP) and demonstrated that especially in VS (as indicated by the exponential receiver operating curve enrichment metric (eROCE)), for the vast majority of kinases the model performance increased compared with the individual fingerprint models. These findings are highlighted in the more challenging DivSets compared with RandSets. The current paper explores the boundaries of inhibitor predictors for individual kinases to enhance VS and ultimately aid the discovery of novel compounds with desirable polypharmacology.

中文翻译：

---

使用高度可信的数据集对激酶抑制进行建模

蛋白激酶形成一类有前途的有希望的药物靶标，并且已经做出了一些努力来预测小分子针对该激酶组的代表性部分的活性。这项研究继续了我们以前的工作（宝拉（Bora）；Avram，S .；丘卡努，我。莱卡（Raica），M。阿夫拉姆（美国）快速有效地分析激酶抑制剂的预测模型。J.化学。Inf。模型。 2016，56，895-905; www.chembioinf.ro）旨在建立和衡量基于配体的激酶抑制剂预测模型的性能。在这里，我们分析了从ChEMBL数据库中提取的具有多个活性点的激酶抑制剂对，并确定了不一致的主要来源。我们的结果表明较低的IC ₅₀值通常受误差影响较小，并且可以更准确地反映分子与靶标之间的结构-活性关系，非常适合进行定量的结构-活性关系研究。此外，我们使用无偏目标特异活性点对104种激酶的活性进行了建模。在虚拟筛选（VS）和使用外部随机分类任务的容差区间（TI）（95％/ 95％）的基础上，对基于扩展连接指纹（ECFP4）和二维药效团指纹（PFP）构建的预测变量的性能进行了比较（RandSets）和基于多样性的（DivSets）测试集。我们发现，这两种编码在VS中的不同激酶上的性能均优于彼此，并且PFP模型在对活性成分进行分类时始终表现出更好的性能（更高的灵敏度）。接下来，我们将两种编码合并为一个编码（PFPECFP），并证明尤其是在VS中（如指数接收器操作曲线富集度（eROCE）所示），对于绝大多数激酶，模型性能均比单个编码有所提高。指纹模型。与RandSets相比，在更具挑战性的DivSets中突出了这些发现。本论文探索了单个激酶抑制剂预测因子的边界，以增强VS并最终帮助发现具有理想多药理学性质的新型化合物。