Improved CoMFA modeling by optimization of settings

Shane D. Peterson

doi:10.1021/ci049612j

2005-11-03

PRE

Shane D. Peterson

Wesley Schaal

Anders Karlén

DOI：10.1021/ci049612j

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摘要

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The possibility of improving the predictive ability of comparative molecular field analysis (CoMFA) by settings optimization has been evaluated to show that CoMFA predictive ability can be improved. Ten different CoMFA settings are evaluated, producing a total of 6120 models. This method has been applied to nine different data sets, including the widely used benchmark steroid data set, as well as eight other data sets proposed as QSAR benchmarking data sets by Sutherland et al. (J. Med. Chem. 2004, 47, 5541-5554). All data sets have been studied using training and test sets to allow for both internal (q(2)) and external (r(pred)(2)) predictive ability assessment. CoMFA settings optimization was successful in developing models with improved q(2) and r(pred)(2) as compared to default CoMFA modeling. Optimized CoMFA is compared with comparative molecular similarity indices analysis (CoMSIA) and holographic quantitative structure-activity relationship (HQSAR) models and found to consistently produce models with improved or equivalent q(2) and r(pred)(2). The ability of settings optimization to improve model predictive ability has been validated using both internal and external predictions, and the risk of chance correlation has been evaluated using response variable randomization tests.

Keyword:

MOLECULAR-FIELD ANALYSIS

CORTICOSTEROID-BINDING GLOBULIN

PARTIAL LEAST-SQUARES

3D QSAR

ORBITAL ELECTRONEGATIVITY

THERMOLYSIN INHIBITORS

PREDICTIVE ABILITY

SURFACE PROPERTIES

CONVERTING ENZYME

REGRESSION-MODELS