Simple comparative correlation analyses and quantitative structure–kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug–target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling.
Computational modeling approaches to quantitative structure–binding kinetics relationships in drug discovery / De Benedetti, Pier G.; Fanelli, Francesca. - In: DRUG DISCOVERY TODAY. - ISSN 1359-6446. - 23:7(2018), pp. 1396-1406. [10.1016/j.drudis.2018.03.010]
Computational modeling approaches to quantitative structure–binding kinetics relationships in drug discovery
De Benedetti, Pier G.
;Fanelli, Francesca
2018
Abstract
Simple comparative correlation analyses and quantitative structure–kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug–target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling.File | Dimensione | Formato | |
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