title: Quantitatively integrating molecular structure and bioactivity profile evidence into drug-target relationship analysis.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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 using the integrated similarity, which indicated that the present multi-view based clustering is more efficient by successfully identifying clusters with its members sharing more number of common targets. Analysis in certain classes reveals that mutual complement of the two views for compound description helps to discover missing similar compound when only single view was applied. Then, a large-scale drug virtual screen was performed on 1267 compounds curated from Connectivity Map (CMap) dataset based on the fused similarity, which obtained a better ranking result compared to that of single-view. These comprehensive tests indicated that by combining different data representations; an improved assessment of target-specific compound similarity can be achieved.
  
  
  CONCLUSIONS
  Our study presented an efficient, extendable and quantitative computational model for integration of different compound representations, and expected to provide new clues to improve the virtual drug screening from various pharmacological properties. Scripts, supplementary materials and data used in this study are publicly available at http://lifecenter.sgst.cn/fusion/.
authors.affiliation: Department of Bioinformatics, Tongji University, 200092, Shanghai, China. qiliu@tongji.edu.cn
authors.forename: Tianlei
authors.initials: T
authors.lastname: Xu
authors.forename: Ruixin
authors.initials: R
authors.lastname: Zhu
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D015870:Gene Expression / Q000187:drug effects*; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D013329:Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations