Anin silicoensemble method for lead discovery: decision forest
- 1 August 2005
- journal article
- research article
- Published by Taylor & Francis Ltd in SAR and QSAR in Environmental Research
- Vol. 16 (4), 339-347
- https://doi.org/10.1080/10659360500203022
Abstract
Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.Keywords
This publication has 17 references indexed in Scilit:
- Decision Forest: Combining the Predictions of Multiple Independent Decision Tree ModelsJournal of Chemical Information and Computer Sciences, 2003
- An integrated "4-phase" approach for setting endocrine disruption screening priorities--phase I and II predictions of estrogen receptor binding affinitySAR and QSAR in Environmental Research, 2002
- Estrogenic Activities of 517 Chemicals by Yeast Two-Hybrid Assay.Journal of Health Science, 2000
- Discovery of HIV-1 Integrase Inhibitors by Pharmacophore SearchingJournal of Medicinal Chemistry, 1997
- Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settingsAdvanced Drug Delivery Reviews, 1997
- Chapter 26. Discovery and Identification of Lead Compounds from Combinatorial MixturesPublished by Elsevier BV ,1997
- Analysis of a Large Structure‐Activity Data Set Using Recursive PartitioningQuantitative Structure-Activity Relationships, 1997
- Pattern recognition by means of disjoint principal components modelsPattern Recognition, 1976
- Pattern recognition. Powerful approach to interpreting chemical dataJournal of the American Chemical Society, 1972
- THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMSAnnals of Eugenics, 1936