Molecular Diversity

, Volume 10, Issue 3, pp 301–309

SVM approach for predicting LogP

Full–length paper

DOI: 10.1007/s11030-006-9036-2

Cite this article as:
Liao, Q., Yao, J. & Yuan, S. Mol Divers (2006) 10: 301. doi:10.1007/s11030-006-9036-2

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 predictionmultiple linear regression (MLR)partial least squares (PLS)support vector machines (SVM)

Abbreviations

LogP

the logarithm of the partition coefficient between n-octanol and water

SVM

support vector machines

PLS

partial least squares

MLR

multiple linear regression

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic ChemistryChinese Academy of SciencesShanghaiChina