Three-class classification models of logS and logP derived by using GA-CG-SVM approach. (Q51853312)

14 April 2018

title

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

1 reference

14 April 2018

author name string

Hui Zhang

1

1 reference

14 April 2018

Ming-Li Xiang

2

1 reference

14 April 2018

Chang-Ying Ma

3

1 reference

14 April 2018

Qi Huang

4

1 reference

14 April 2018

Wei Li

5

1 reference

14 April 2018

Yang Xie

6

1 reference

14 April 2018

Yu-Quan Wei

7

1 reference

14 April 2018

Sheng-Yong Yang

8

1 reference

14 April 2018

publication date

31 January 2009

1 reference

14 April 2018

published in

Molecular Diversity

1 reference

14 April 2018

volume

13

1 reference

14 April 2018

issue

2

1 reference

14 April 2018

page(s)

261-268

1 reference

14 April 2018

ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach.

cites work

1 reference

https://api.crossref.org/works/10.1007%2FS11030-009-9108-1

A consensus neural network-based technique for discriminating soluble and poorly soluble compounds.

21 January 2018

1 reference

https://api.crossref.org/works/10.1007%2FS11030-009-9108-1

Differential Shannon entropy analysis identifies molecular property descriptors that predict aqueous solubility of synthetic compounds with high accuracy in binary QSAR calculations.

21 January 2018

1 reference

https://api.crossref.org/works/10.1007%2FS11030-009-9108-1

Prediction of P-glycoprotein substrates by a support vector machine approach.

21 January 2018

1 reference

https://api.crossref.org/works/10.1007%2FS11030-009-9108-1

Feature subset selection for splice site prediction

21 January 2018

1 reference

https://api.crossref.org/works/10.1007%2FS11030-009-9108-1

Computational models to predict aqueous drug solubility, permeability and intestinal absorption

21 January 2018

1 reference

12 December 2020

ADMET in silico modelling: towards prediction paradise?

1 reference

12 December 2020

SVM approach for predicting LogP.

1 reference

12 December 2020

In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values

1 reference

12 December 2020

Assessing the accuracy of prediction algorithms for classification: an overview

1 reference

12 December 2020

An integrated scheme for feature selection and parameter setting in the support vector machine modeling and its application to the prediction of pharmacokinetic properties of drugs

1 reference

12 December 2020

Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA-CG-SVM method.

1 reference

12 December 2020

Prediction of physicochemical properties based on neural network modelling

1 reference

12 December 2020

Prediction of aqueous solubility of organic compounds using a quantitative structure-property relationship.

1 reference

12 December 2020

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

1 reference

12 December 2020

Recent progress in the computational prediction of aqueous solubility and absorption

1 reference

12 December 2020

High-Throughput,In SilicoPrediction of Aqueous Solubility Based on One- and Two-Dimensional Descriptors

1 reference

12 December 2020

Global and Local Computational Models for Aqueous Solubility Prediction of Drug-Like Molecules

1 reference

12 December 2020

Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods.

1 reference

12 December 2020

A comparison of methods for multiclass support vector machines

1 reference

12 December 2020

Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation.

1 reference

12 December 2020

Substructure and whole molecule approaches for calculating log P.

1 reference

12 December 2020

10.1007/S11030-009-9108-1

Identifiers

DOI

1 reference

14 April 2018

1 reference

14 April 2018