Abstract：
A novel decision-making procedure is proposed here for the first time to identify active/inactive and selective/non-selective dual inhibitors using consensus approaches and pools of k-nearest neighbours (kNN) classifications instead of individual models. Dual BRD4/PLK1 inhibition with adequate selectivity is a potential therapeutic strategy for targeting tumour cells in high-risk patients. We report the unique way to identify both active and selective dual BRD4/PLK1 inhibitors using consensus and kNN strategies together with two sources of receptor-based and ligand-based information which are the ranked binding energies of residues and important molecular features, respectively. The results of consensus approaches were compared with the results of individual kNN models. The chemical space similarity was measured using three different distance functions to increase the reliability. All activity and selectivity classification models were validated using cross-validation and y-randomization tests. The outcomes show that consensus approaches can increase the reliability and accuracy of active/inactive or selective/non-selective detections up to 90%. Consensus approaches also reached more balanced values of sensitivity and specificity compared to the individual kNN models because of the compensation in the integration of diverse sources of information.
摘要：
这里首次提出了一种新颖的决策程序，以使用共识方法和k近邻（kNN）分类库而不是单个模型来识别活性/非活性和选择性/非选择性双重抑制剂。具有足够选择性的双重BRD4/PLK1抑制是针对高危患者肿瘤细胞的潜在治疗策略。我们报告了使用共识和kNN策略以及两种基于受体和基于配体的信息来源来鉴定活性和选择性双重BRD4/PLK1抑制剂的独特方法，这些信息是残基的排名结合能和重要的分子特征。分别。将共识方法的结果与单个kNN模型的结果进行了比较。使用三个不同的距离函数测量化学空间相似性以增加可靠性。所有活性和选择性分类模型均使用交叉验证和y-随机化测试进行验证。结果表明，共识方法可以将主动/非主动或选择性/非选择性检测的可靠性和准确性提高90％。与单个kNN模型相比，共识方法还达到了灵敏度和特异性的更平衡值，因为在整合各种信息源方面具有补偿作用。
