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Modelling Inhalational Anaesthetics Using Bayesian Feature Selection and QSAR Modelling Methods
Author(s) -
Manallack David T.,
Burden Frank R.,
Winkler David A.
Publication year - 2010
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.201000056
Subject(s) - quantitative structure–activity relationship , feature selection , molecular descriptor , context (archaeology) , bayesian probability , artificial intelligence , selection (genetic algorithm) , computer science , feature (linguistics) , machine learning , model selection , bayesian inference , data mining , biology , linguistics , philosophy , paleontology
The development of robust and predictive QSAR models is highly dependent on the use of molecular descriptors that contain information relevant to the property being modelled. Selection of these relevant features from a large pool of possibilities is difficult to achieve effectively. Modern Bayesian methods provide substantial advantages over conventional feature selection methods for feature selection and QSAR modelling. We illustrate the importance of descriptor choice and the beneficial properties of Bayesian methods to select context‐dependent relevant descriptors and build robust QSAR models, using data on anaesthetics. Our results show the effectiveness of Bayesian feature selection methods in choosing the best descriptors when these are mixed with less informative descriptors. They also demonstrate the efficacy of the Abraham descriptors and identify deficiencies in ParaSurf descriptors for modelling anaesthetic action.