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Deep Ranking Analysis by Power Eigenvectors (DRAPE): a polypharmacology case study
Chemometrics and Intelligent Laboratory Systems ( IF 3.9 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.chemolab.2020.104001
Cecile Valsecchi , Davide Ballabio , Viviana Consonni , Roberto Todeschini

Abstract Multi-criteria decision making processes comprehend several ranking methods able to handle multiple and often conflicting criteria and sorting objects according to a definition of the optimality direction for each criterion. In the field of polypharmacology it can be useful for virtual screening or prioritization purposes to rank molecules distinguishing the most multi-target ones, i.e. molecules able to interact with different targets, from the selective or inactive ones. In this work, the Deep Ranking Analysis by Power Eigenvectors approach is applied to a small set of molecules characterized by their half maximal binding concentrations for seven different nuclear receptors. The existence of a correspondence between the DRAPE ranking and a manually grouping of molecules based on their multi-target behaviour was verified. Moreover, a comparison between DRAPE rankings and those obtained from five traditional methods was carried out in order to highlight the main similarities and the differences of the approaches.

中文翻译:

Power Eigenvectors (DRAPE) 的深度排名分析:多药理学案例研究

摘要 多准则决策过程包含几种排序方法,能够处理多个且经常相互冲突的准则,并根据每个准则的最优方向的定义对对象进行排序。在多药理学领域,对分子进行排序以区分最多多靶点的分子(即能够与不同靶点相互作用的分子)与选择性的或无活性的分子对于虚拟筛选或优先排序是有用的。在这项工作中,Power Eigenvectors 方法的深度排名分析应用于一小组分子,其特征在于它们对七种不同核受体的半数最大结合浓度。验证了 DRAPE 排名与基于分子的多目标行为的手动分组之间的对应关系。而且,
更新日期:2020-08-01
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