Binary Classification of Aqueous Solubility Using Support Vector Machines with Reduction and Recombination Feature Selection-Reference-Cited by-同舟云学术

Binary Classification of Aqueous Solubility Using Support Vector Machines with Reduction and Recombination Feature Selection

Published:2011-01-07 Issue:2 Volume:51 Page:229-236
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Cheng Tiejun¹,Li Qingliang¹,Wang Yanli¹,Bryant Stephen H.¹

Affiliation:

1. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, Maryland 20894, United States

Funder

National Institutes of Health

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci100364a

Reference59 articles.

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2. ADMET in silico modelling: towards prediction paradise?

3. In silicoprediction of aqueous solubility

4. Recent progress in the computational prediction of aqueous solubility and absorption

5. Estimation of Aqueous Solubility for a Diverse Set of Organic Compounds Based on Molecular Topology

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