Literature DB >> 17031534

SVM approach for predicting LogP.

Quan Liao1, Jianhua Yao, Shengang Yuan.   

Abstract

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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Year:  2006        PMID： 17031534     DOI： 10.1007/s11030-006-9036-2

Source DB:  PubMed          Journal:  Mol Divers        ISSN： 1381-1991            Impact factor:   2.943


  16 in total

1.  Prediction of protein retention times in anion-exchange chromatography systems using support vector regression.

Authors:  Minghu Song; Curt M Breneman; Jinbo Bi; N Sukumar; Kristin P Bennett; Steven Cramer; Nihal Tugcu
Journal:  J Chem Inf Comput Sci       Date:  2002 Nov-Dec

Review 2.  Chemical substructures in drug discovery.

Authors:  Cédric Merlot; Daniel Domine; Christophe Cleva; Dennis J Church
Journal:  Drug Discov Today       Date:  2003-07-01       Impact factor: 7.851

3.  Analyses of the partition coefficient, log P, using ab initio MO parameter and accessible surface area of solute molecules.

Authors:  Hiroshi Chuman; Atsushi Mori; Hideji Tanaka; Chisako Yamagami; Toshio Fujita
Journal:  J Pharm Sci       Date:  2004-11       Impact factor: 3.534

4.  A universal molecular descriptor system for prediction of logP, logS, logBB, and absorption.

Authors:  Hongmao Sun
Journal:  J Chem Inf Comput Sci       Date:  2004 Mar-Apr

5.  Automatic log P estimation based on combined additive modeling methods.

Authors:  T Suzuki; Y Kudo
Journal:  J Comput Aided Mol Des       Date:  1990-06       Impact factor: 3.686

6.  Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis.

Authors:  Feng Luan; Ruisheng Zhang; Chunyan Zhao; Xiaojun Yao; Mancang Liu; Zhide Hu; Botao Fan
Journal:  Chem Res Toxicol       Date:  2005-02       Impact factor: 3.739

7.  Whole-molecule calculation of log p based on molar volume, hydrogen bonds, and simulated 13C NMR spectra.

Authors:  Laura K Schnackenberg; Richard D Beger
Journal:  J Chem Inf Model       Date:  2005 Mar-Apr       Impact factor: 4.956

8.  Chance factors in studies of quantitative structure-activity relationships.

Authors:  J G Topliss; R P Edwards
Journal:  J Med Chem       Date:  1979-10       Impact factor: 7.446

9.  A partition coefficient calculation method with the SFED model.

Authors:  Youngyong In; Han Ha Chai; Kyoung Tai No
Journal:  J Chem Inf Model       Date:  2005 Mar-Apr       Impact factor: 4.956

10.  Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions.

Authors:  Vladimir V Zernov; Konstantin V Balakin; Andrey A Ivaschenko; Nikolay P Savchuk; Igor V Pletnev
Journal:  J Chem Inf Comput Sci       Date:  2003 Nov-Dec
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  11 in total

1.  Calculating Partition Coefficients of Small Molecules in Octanol/Water and Cyclohexane/Water.

Authors:  Caitlin C Bannan; Gaetano Calabró; Daisy Y Kyu; David L Mobley
Journal:  J Chem Theory Comput       Date:  2016-08-01       Impact factor: 6.006

2.  Prediction and interpretation of the lipophilicity of small peptides.

Authors:  Alessia Visconti; Giuseppe Ermondi; Giulia Caron; Roberto Esposito
Journal:  J Comput Aided Mol Des       Date:  2015-01-11       Impact factor: 3.686

3.  Pitfalls of supervised feature selection.

Authors:  Pawel Smialowski; Dmitrij Frishman; Stefan Kramer
Journal:  Bioinformatics       Date:  2009-10-29       Impact factor: 6.937

Review 4.  Hydrophobicity--shake flasks, protein folding and drug discovery.

Authors:  Aurijit Sarkar; Glen E Kellogg
Journal:  Curr Top Med Chem       Date:  2010       Impact factor: 3.295

5.  Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines.

Authors:  Quan Liao; Jianhua Yao; Shengang Yuan
Journal:  Mol Divers       Date:  2007-04-11       Impact factor: 2.943

6.  Three-class classification models of logS and logP derived by using GA-CG-SVM approach.

Authors:  Hui Zhang; Ming-Li Xiang; Chang-Ying Ma; Qi Huang; Wei Li; Yang Xie; Yu-Quan Wei; Sheng-Yong Yang
Journal:  Mol Divers       Date:  2009-01-31       Impact factor: 3.364

7.  Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection.

Authors:  Tiejun Cheng; Qingliang Li; Yanli Wang; Stephen H Bryant
Journal:  J Chem Inf Model       Date:  2011-01-07       Impact factor: 4.956

8.  ClassicalGSG: Prediction of log P using classical molecular force fields and geometric scattering for graphs.

Authors:  Nazanin Donyapour; Matthew Hirn; Alex Dickson
Journal:  J Comput Chem       Date:  2021-03-30       Impact factor: 3.672

9.  Large-scale ligand-based predictive modelling using support vector machines.

Authors:  Jonathan Alvarsson; Samuel Lampa; Wesley Schaal; Claes Andersson; Jarl E S Wikberg; Ola Spjuth
Journal:  J Cheminform       Date:  2016-08-10       Impact factor: 5.514

10.  Solvation Thermodynamics in Different Solvents: Water-Chloroform Partition Coefficients from Grid Inhomogeneous Solvation Theory.

Authors:  Johannes Kraml; Florian Hofer; Anna S Kamenik; Franz Waibl; Ursula Kahler; Michael Schauperl; Klaus R Liedl
Journal:  J Chem Inf Model       Date:  2020-07-20       Impact factor: 6.162
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