The BioAssay network and its implications to future therapeutic discovery

BMC Bioinformatics

Proceedings

Jintao Zhang¹  Jun Huan²  Gerald H Lushington³ 

[1] Center for Bioinformatics, University of Kansas, 66045, Lawrence, KS, USA;Department of Electrical Engineering & Computer Science, University of Kansas, 66045, Lawrence, KS, USA;Molecular Graphics & Modeling Lab, University of Kansas, 66045, Lawrence, KS, USA;

关键词: Cluster Coefficient;    Essential Gene;    Bipartite Network;    Average Cluster Coefficient;    DrugBank Database;   

DOI  :  10.1186/1471-2105-12-S5-S1

来源: Springer

【 摘 要 】

BackgroundDespite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the Molecular Libraries Initiative (MLI) in 2003 to enlarge the pool for potential drug targets, especially from the “undruggable” part of human genome, and potential drug candidates from much broader types of drug-like small molecules. All results are being made publicly available in a web portal called PubChem.ResultsIn this paper we construct a network from bioassay data in PubChem, apply network biology concepts to characterize this bioassay network, integrate information from multiple biological databases (e.g. DrugBank, OMIM, and UniHI), and systematically analyze the potential of bioassay targets being new drug targets in the context of complex biological networks. We propose a model to quantitatively prioritize this druggability of bioassay targets, and literature evidence was found to confirm our prioritization of bioassay targets at a roughly 70% accuracy.ConclusionsOur analysis provide some measures of the value of the MLI data as a resource for both basic chemical biology research and future therapeutic discovery.

【 授权许可】

Unknown   
© Zhang et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.