Modelling Inhalational Anaesthetics Using Bayesian Feature Selection and QSAR Modelling Methods

ChemMedChem - Tập 5 Số 8 - Trang 1318-1323 - 2010
David T. Manallack1, Frank R. Burden2,3, David A. Winkler2,1
1Monash Institute for Pharmaceutical Science, 381 Royal Parade, Parkville, VIC 3152 (Australia)
2CSIRO Molecular & Health Technologies, Bayview Avenue, Clayton, VIC 3168 (Australia),
3Scimetrics, Harrow Enterprises Pty. Ltd. 548 Canning Street, Carlton North, VIC 3054 (Australia)

Tóm tắt

AbstractThe 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.

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ChemMedChem

Tập 5 Số 8

1318-1323

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