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Drug discovery / Cheminformatics / Machine learning / Medicinal chemistry / Quantitative structure–activity relationship / Decision tree learning / In silico / Chemical library / Virtual screening / Pharmaceutical sciences / Science / Pharmacology


SAR and QSAR in Environmental Research, Vol. 16, No. 4, August 2005, 339–347 An in silico ensemble method for lead discovery: decision forest H. HONG†, W. TONG‡*, Q. XIE†, H. FANG† and R. PERKINS† †Divisio
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City

Pensecola / Jefferson / New York / /

Company

SETAC Press / Taylor & Francis Group Ltd / /

Country

United States / /

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Facility

National Center / L.H. Hall / /

IndustryTerm

silico tool / pharmaceutical industry / software tools / neural networks / decision tree algorithm / chemical abstract service / food additives / /

NaturalFeature

tree* Decision forest / Decision forest / /

Organization

National Center for Toxicological Research / Center for Toxicoinformatics / U.S. Food and Drug Administration / Division of Bioinformatics / Division of Systems Toxicology / /

Person

R. Perkins / M. Blair / T.R. Burke Jr. / /

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Position

*Corresponding author / Walker / Model development The reliability / /

ProvinceOrState

New York / Arkansas / /

PublishedMedium

Environmental Research / /

Technology

combinatorial chemistry / Bioinformatics / drug discovery / high-throughput screening / decision tree algorithm / 2.1 Algorithm / drug development / /

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http /

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