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Literature DB >> 16454756

Decision tree methods in pharmaceutical research.

Abstract

Decision trees are among the most popular of the new statistical learning methods being used in the pharmaceutical industry for predicting quantitative structure-activity relationships. This article reviews applications of decision trees in drug discovery research and extensions to the basic algorithm using hybrid or ensemble methods that improve prediction accuracy.

Mesh：

Year: 2006 PMID： 16454756 DOI： 10.2174/156802606775193301

Source DB: PubMed Journal: Curr Top Med Chem ISSN： 1568-0266 Impact factor: 3.295

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Cited

5 in total

1. Computational analysis of HIV-1 protease protein binding pockets.

Authors: Gene M Ko; A Srinivas Reddy; Sunil Kumar; Barbara A Bailey; Rajni Garg
Journal: J Chem Inf Model Date: 2010-10-25 Impact factor: 4.956

2. Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of Tipranavir analogs for HIV protease inhibition.

Authors: A Srinivas Reddy; Sunil Kumar; Rajni Garg
Journal: J Mol Graph Model Date: 2010-03-24 Impact factor: 2.518

Decision tree methods in pharmaceutical research.

1. Computational analysis of HIV-1 protease protein binding pockets.

2. Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of Tipranavir analogs for HIV protease inhibition.

3. The use of classification trees for bioinformatics.

4. Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data.

5. Predicting p Ka values from EEM atomic charges.