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.

Valsecchi, C., Ballabio, D., Consonni, V., Todeschini, R. (2020). Deep Ranking Analysis by Power Eigenvectors (DRAPE): A polypharmacology case study. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 203, 104001 [10.1016/j.chemolab.2020.104001].

Deep Ranking Analysis by Power Eigenvectors (DRAPE): A polypharmacology case study

Valsecchi, Cecile;Ballabio, Davide;Consonni, Viviana;Todeschini, Roberto
2020

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.
Articolo in rivista - Articolo scientifico
ranking; chemometrics
English
17-mar-2020
2020
203
104001
104001
reserved
Valsecchi, C., Ballabio, D., Consonni, V., Todeschini, R. (2020). Deep Ranking Analysis by Power Eigenvectors (DRAPE): A polypharmacology case study. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 203, 104001 [10.1016/j.chemolab.2020.104001].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/278459
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