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Article

Screening for New Antidepressant Leads of Multiple Activities by Support Vector Machines

Supporting Info

Zsolt Lepp, Takashi Kinoshita, and Hiroshi Chuman*

Institute of Health Biosciences, The University of Tokushima, Shomachi, Tokushima 770-8505, Japan

J. Chem. Inf. Model., 2006, 46 (1), pp 158–167

DOI: 10.1021/ci050301y

Publication Date (Web): November 12, 2005

Corresponding author phone and fax: +81-88-633-9508; e-mail: hchuman@ph.tokushima-u.ac.jp.

Abstract

Virtual screening was carried out against 21 biological targets related to depression by support vector machine classification using the same atom-type descriptors. The models were effective as 0.2−0.8 of theoretical enrichments of the external test data sets could be achieved, depending on the target. The set of predicted active molecules had large diversity and contained examples with high dissimilarity to the compounds of training sets. Filtering the database of known antidepressants by all 21 models it was found that on average compounds were classified active for 2.3 targets.

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Published In Issue January 23, 2006
Received July 25, 2005

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Article

Screening for New Antidepressant Leads of Multiple Activities by Support Vector Machines

Abstract

Tools

SciFinder Links

History

Recommend & Share

Related Content

Enrichment of High-Throughput Screening Data with Increasing Levels of Noise Using Support Vector Machines, Recursive Partitioning, and Laplacian-Modified Naive Bayesian Classifiers

New Scoring Functions for Virtual Screening from Molecular Dynamics Simulations with a Quantum-Refined Force-Field (QRFF-MD). Application to Cyclin-Dependent Kinase 2

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