SVM approach for predicting LogP

@article{Liao2006SVMAF,
  title={SVM approach for predicting LogP},
  author={Q. Liao and J. Yao and S. Yuan},
  journal={Molecular Diversity},
  year={2006},
  volume={10},
  pages={301-309}
}
  • Q. Liao, J. Yao, S. Yuan
  • Published 2006
  • Mathematics, Medicine
  • Molecular Diversity
  • SummaryThe 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. 
    27 Citations

    Figures, Tables, and Topics from this paper

    Explore Further: Topics Discussed in This Paper

    Three-class classification models of logS and logP derived by using GA–CG–SVM approach
    • 22
    Prediction and interpretation of the lipophilicity of small peptides
    • 3
    • PDF
    Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines
    • 21
    Large-scale ligand-based predictive modelling using support vector machines
    • 11
    • Highly Influenced
    • PDF
    Assessment of the chromatographic lipophilicity of eight cephalosporins on different stationary phases
    • 8
    Hydrophobicity--shake flasks, protein folding and drug discovery.
    • 74

    References

    SHOWING 1-10 OF 40 REFERENCES
    A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption
    • Hongmao Sun
    • Chemistry, Computer Science
    • J. Chem. Inf. Model.
    • 2004
    • 107
    Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions
    • 143
    • PDF
    Automatic log P estimation based on combined additive modeling methods
    • 128
    Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression
    • 197
    • PDF
    Prediction of Protein Retention Times in Anion-Exchange Chromatography Systems Using Support Vector Regression
    • 116
    • PDF
    A New Atom-Additive Method for Calculating Partition Coefficients
    • 338
    Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis.
    • 53
    • PDF
    Generalized Fragment-Substructure Based Property Prediction Method
    • M. Clark
    • Medicine, Computer Science
    • J. Chem. Inf. Model.
    • 2005
    • 41
    Autocorrelation modeling of lipophilicity with a back-propagation neural network
    • 17