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In Silico Log P Prediction for a Large Data Set with Support Vector Machines, Radial Basis Neural Networks and Multiple Linear Regression
Author(s) -
Chen HaiFeng
Publication year - 2009
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2009.00840.x
Subject(s) - support vector machine , artificial neural network , radial basis function , linear regression , correlation coefficient , basis (linear algebra) , quantitative structure–activity relationship , basis function , test set , mathematics , regression analysis , artificial intelligence , pattern recognition (psychology) , computer science , machine learning , mathematical analysis , geometry
Oil/water partition coefficient (log P ) is one of the key points for lead compound to be drug. In silico log P models based solely on chemical structures have become an important part of modern drug discovery. Here, we report support vector machines, radial basis function neural networks, and multiple linear regression methods to investigate the correlation between partition coefficient and physico‐chemical descriptors for a large data set of compounds. The correlation coefficient r 2 between experimental and predicted log P for training and test sets by support vector machines, radial basis function neural networks, and multiple linear regression is 0.92, 0.90, and 0.88, respectively. The results show that non‐linear support vector machines derives statistical models that have better prediction ability than those of radial basis function neural networks and multiple linear regression methods. This indicates that support vector machines can be used as an alternative modeling tool for quantitative structure–property/activity relationships studies.