Modelling inhalational anaesthetics using bayesian feature selection and QSAR modelling methods.
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.
Monash Institute for Pharmaceutical Science, Parkville, VIC, Australia.
David T
DT
Manallack
Frank R
FR
Burden
David A
DA
Winkler
D000777:Anesthetics / Q000737:chemistry* / Q000494:pharmacology; D018685:Anesthetics, Inhalation; D001499:Bayes Theorem; D003292:Convulsants / Q000737:chemistry* / Q000494:pharmacology; D016014:Linear Models; D021281:Quantitative Structure-Activity Relationship
D000777:Anesthetics; D018685:Anesthetics, Inhalation; D003292:Convulsants