Full–length paper

Molecular Diversity

, Volume 10, Issue 3, pp 301-309

First online:

SVM approach for predicting LogP

  • Quan LiaoAffiliated withDepartment of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
  • , Jianhua YaoAffiliated withDepartment of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences Email author 
  • , Shengang YuanAffiliated withDepartment of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences

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Summary

The logarithm of the partition coefficient between n-octanol and water (logP) is an important parameter for drug discovery. Based upon the comparison of several prediction logP models, i.e. Support Vector Machines (SVM), Partial Least Squares (PLS) and Multiple Linear Regression (MLR), the authors reported SVM model is the best one in this paper.

Key words

LogP prediction multiple linear regression (MLR) partial least squares (PLS) support vector machines (SVM)