Predicting minimum alveolar concentration (MAC) of anesthetic agents by statistical modeling methods and theoretical descriptors derived from electrostatic potentials on molecular surface

Abstract

Some up-to-date modeling techniques, which include nonlinear support vector machine (SVM), least-squares support vector machine (LSSVM), random forest (RF) and Gaussian process (GP), together with linear methods (multiple linear regression (MLR) and partial least-squares regression (PLS)) were employed to establish quantitative relationships between the structural descriptors and the minimum alveolar concentration (MAC). It has been found that a set of physical quantities extracted from electrostatic potential on molecular surface, together with some usual quantum chemical descriptors, such as the energy level of the frontier molecular orbital, can be well used to construct the quantitative structure–activity relationships for the present data set. Systematical validations including internal 10-fold cross-validation, the validation for external test set, as well as a more rigorous Monte Carlo cross-validation were also performed to confirm the reliability of the constructed models. Among these modeling methods, the GP, which can handle linear and nonlinear–hybrid relationship through a mixed covariance function, shows the best fitting and predictive abilities. The coefficient of determination rpred2 and root mean square error of prediction (RMSEP) for the external test set are 0.911 and 0.475, respectively.