Prediction of the Isoelectric Point of an Amino Acid Based on GA-PLS and SVMs

Abstract

The support vector machine (SVM), as a novel type of a learning machine, for the first time, was used to develop a QSPR model that relates the structures of 35 amino acids to their isoelectric point. Molecular descriptors calculated from the structure alone were used to represent molecular structures. The seven descriptors selected using GA-PLS, which is a sophisticated hybrid approach that combines GA as a powerful optimization method with PLS as a robust statistical method for variable selection, were used as inputs of RBFNNs and SVM to predict the isoelectric point of an amino acid. The optimal QSPR model developed was based on support vector machines, which showed the following results: the root-mean-square error of 0.2383 and the prediction correlation coefficient R ) 0.9702 were obtained for the whole data set. Satisfactory results indicated that the GA-PLS approach is a very effective method for variable selection, and the support vector machine is a very promising tool for the nonlinear approximation.

QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine

“…Due to its remarkable generalization performance, the SVM has attracted attention and gained extensive application, such as pattern recognition problems, 16,17 drug design, 18 QSAR, 19 and quantitativestructure-propertyrelationship(QSPR)analysis. [20][21][22][23] In the present work, the CODESSA program was used for the calculation of the descriptors and for the statistical analysis to obtain the multiparameter QSAR equations describing the binding affinities of drugs. The heuristic method (HM) in the CODESSA program and the SVM were utilized to establish a quantitative linear and nonlinear relationship between the binding affinity and the molecular structure, respectively.…”

Quantitative Prediction of logk of Peptides in High‐Performance Liquid Chromatography Based on Molecular Descriptors by Using the Heuristic Method and Support Vector Machine.

“…Due to its remarkable generalization performance, the SVM has gained much attention and extensive applications. 11,[14][15][16][17][18][19][20][21][22] Another important problem for the QSPR applications is the numerical representation (often called molecular descriptor) of the chemical structure. The built model performance and the accuracy of the results are strongly dependent on the way the structural representation was performed.…”

QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-diarylimidazoles based on support vector machines

“…Due to its remarkable generalization performance, the SVM has attracted attention and gained extensive application in pattern recognition and regression problems [30,[32][33][34][35][36][37][38][39].…”