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. 2014 Jun 18:6:33.
doi: 10.1186/1758-2946-6-33. eCollection 2014.

In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion

Xian Liu  1 , Yuan Xu  1 , Shanshan Li  1 , Yulan Wang  1 , Jianlong Peng  1 , Cheng Luo  1 , Xiaomin Luo  1 , Mingyue Zheng  1 , Kaixian Chen  2 , Hualiang Jiang  2
Affiliations

In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion

Xian Liu et al. J Cheminform. .

Abstract

Background: Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound.

Results: We tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities.

Conclusions: With the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery.

Keywords: Big data; Data fusion; Molecular fingerprints; Similarity searching; Target fishing.

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Figures

Figure 1
Figure 1
Plot to show the distribution of the number of active ligands against the number of targets per ligand.
Figure 2
Figure 2
Plot to show the distribution of average PR’ against the targets with increasing number of reference ligands.PR’ is given by the averaged precision values PRi from the ranking places 1 to m. Here, m for a query ligand is the number of its interacting targets.
Figure 3
Figure 3
Plot to show the change of PR’ with increasing percent of the reference ligands in use.
Figure 4
Figure 4
The bar plot showing the variation of PR’ versus the similarity of a query to its closest neighbor in its corresponding reference ligand set. This analysis is based on the test set containing 2665 query ligands.
Figure 5
Figure 5
Comparison of PR n by 3NN and SEA for the drugs from: (A) DrugBank set and (B) TTD set; Comparison of RE n by 3NN and SEA for the drugs from: (C) DrugBank set and (D) TTD set.
Figure 6
Figure 6
Comparison of F n by 3NN and SEA for the drugs from: (A) DrugBank set and (B) TTD set.
Figure 7
Figure 7
The flowchart of the ligand-based similarity-ranking scheme with data fusion.
See this image and copyright information in PMC

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