SCRAMBLE'N'GAMBLE: a tool for fast and facile generation of random data for statistical evaluation of QSAR models

Chemické Zvesti
Piotr F J Lipiński, Przemysław Szurmak

Abstract

A common practice in modern QSAR modelling is to derive models by variable selection methods working on large descriptor pools. As pointed out previously, this is intrinsically burdened with the risk of finding random correlations. Therefore it is desirable to perform tests showing the performance of models built on random data. In this contribution, we introduce a simple and freely available software tool SCRAMBLE'N'GAMBLE that is aimed at facilitating data preparation for y-randomization and pseudo-descriptors tests. Then, four close-to-real-world modelling situations are analysed. The tests indicate what the quality of obtained QSAR models is like in comparison to chance models derived from random data. The non-randomness is not the only requirement for a good QSAR model, however, it is a good practice to consider it together with internal statistical parameters and possible physical interpretations of a model.

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Citations

Jul 23, 2019·SAR and QSAR in Environmental Research· DanishuddinN Subbarao
Mar 5, 2021·Journal of Chemical Information and Modeling·Maria-Anna TrapotsiAndreas Bender
Jul 1, 2021·Molecular Diversity·Philipe Oliveira FernandesVinícius Gonçalves Maltarollo
Nov 28, 2020·Journal of Chemical Information and Modeling·Elena L CáceresMichael J Keiser

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Software Mentioned

Studio
GAMBLE
Accelrys
Discovery
’ GAMBLE
SCRAMBLE GAMBLE
Accelrys Discovery Studio
DRAGON
SCRAMBLE
QSAR

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