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SVM approach for predicting LogP

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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.

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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

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Authors and Affiliations

  1. Department of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 354, Fenglin Road, 200032, Shanghai, China

    Quan Liao, Jianhua Yao & Shengang Yuan

Authors
  1. Quan Liao
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  2. Jianhua Yao
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  3. Shengang Yuan
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Correspondence to Jianhua Yao.

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Liao, Q., Yao, J. & Yuan, S. SVM approach for predicting LogP. Mol Divers 10, 301–309 (2006). https://doi.org/10.1007/s11030-006-9036-2

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  • DOI: https://doi.org/10.1007/s11030-006-9036-2

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