Quantitatively integrating molecular structure and bioactivity profile evidence into drug-target relationship analysis.

BMC Bioinformatics
Tianlei XuZhiwei Cao

Abstract

Public resources of chemical compound are in a rapid growth both in quantity and the types of data-representation. To comprehensively understand the relationship between the intrinsic features of chemical compounds and protein targets is an essential task to evaluate potential protein-binding function for virtual drug screening. In previous studies, correlations were proposed between bioactivity profiles and target networks, especially when chemical structures were similar. With the lack of effective quantitative methods to uncover such correlation, it is demanding and necessary for us to integrate the information from multiple data sources to produce an comprehensive assessment of the similarity between small molecules, as well as quantitatively uncover the relationship between compounds and their targets by such integrated schema. In this study a multi-view based clustering algorithm was introduced to quantitatively integrate compound similarity from both bioactivity profiles and structural fingerprints. Firstly, a hierarchy clustering was performed with the fused similarity on 37 compounds curated from PubChem. Compared to clustering in a single view, the overall common target number within fused classes has been improved by...Continue Reading

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Citations

Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·James HensmanNeil D Lawrence
Jun 11, 2015·BioMed Research International·Yan-Fen Dai, Xing-Ming Zhao
Jun 18, 2016·Journal of Bioinformatics and Computational Biology·Nolen Joy Perualila-TanAdetayo Kasim

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