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Quantitative prediction of selectivity between the A1 and A2A adenosine receptors
Journal of Cheminformatics ( IF 8.6 ) Pub Date : 2020-05-13 , DOI: 10.1186/s13321-020-00438-3
Lindsey Burggraaff , Herman W. T. van Vlijmen , Adriaan P. IJzerman , Gerard J. P. van Westen

The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, selectivity is often deduced from bioactivity predictions of a ligand for multiple targets (individual machine learning models). Here we show that modeling selectivity directly, by using the affinity difference between two drug targets as output value, leads to more accurate selectivity predictions. We test multiple approaches on a dataset consisting of ligands for the A1 and A2A adenosine receptors (among others classification, regression, and we define different selectivity classes). Finally, we present a regression model that predicts selectivity between these two drug targets by directly training on the difference in bioactivity, modeling the selectivity-window. The quality of this model was good as shown by the performances for fivefold cross-validation: ROC A1AR-selective 0.88 ± 0.04 and ROC A2AAR-selective 0.80 ± 0.07. To increase the accuracy of this selectivity model even further, inactive compounds were identified and removed prior to selectivity prediction by a combination of statistical models and structure-based docking. As a result, selectivity between the A1 and A2A adenosine receptors was predicted effectively using the selectivity-window model. The approach presented here can be readily applied to other selectivity cases.

中文翻译:

A 1和A 2A腺苷受体之间选择性的定量预测

由于脱靶相互作用导致不良反应,常常阻碍药物的开发。因此,评估配体选择性的计算方法备受关注。目前,选择性通常是根据配体对多个目标的生物活性预测得出的(各个机器学习模型)。在这里,我们表明,通过使用两个药物靶标之间的亲和力差异作为输出值,直接对选择性进行建模可导致更准确的选择性预测。我们在由A1和A2A腺苷受体的配体组成的数据集中测试了多种方法(除其他分类,回归方法外,我们还定义了不同的选择性类别)。最后,我们提供了一种回归模型,该模型通过直接训练生物活性的差异来预测这两种药物靶标之间的选择性,建模选择性窗口。如五重交叉验证的性能所示,此模型的质量良好:ROC A1AR选择性0.88±0.04和ROC A2AAR选择性0.80±0.07。为了进一步提高此选择性模型的准确性,在选择性预测之前,通过统计模型和基于结构的对接的组合来识别并去除非活性化合物。结果,使用选择性-窗口模型有效地预测了A1和A2A腺苷受体之间的选择性。这里介绍的方法可以很容易地应用于其他选择性情况。为了进一步提高此选择性模型的准确性,在选择性预测之前,通过统计模型和基于结构的对接的组合来识别并去除非活性化合物。结果,使用选择性-窗口模型有效地预测了A1和A2A腺苷受体之间的选择性。这里介绍的方法可以很容易地应用于其他选择性情况。为了进一步提高此选择性模型的准确性,在选择性预测之前,通过统计模型和基于结构的对接的组合来识别并去除非活性化合物。结果,使用选择性-窗口模型有效地预测了A1和A2A腺苷受体之间的选择性。这里介绍的方法可以很容易地应用于其他选择性情况。
更新日期:2020-05-13
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