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The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1758-2946-6-33","type":"journal-article","created":{"date-parts":[[2014,6,18]],"date-time":"2014-06-18T01:03:02Z","timestamp":1403053382000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["In Silicotarget fishing: addressing a \u201cBig Data\u201d problem by ligand-based similarity rankings with data fusion"],"prefix":"10.1186","volume":"6","author":[{"given":"Xian","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yuan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Shanshan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yulan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jianlong","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Xiaomin","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Mingyue","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Kaixian","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hualiang","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,6,18]]},"reference":[{"key":"608_CR1","first-page":"297","volume":"13","author":"ADW Boran","year":"2010","unstructured":"Boran ADW, Iyengar R: Systems approaches to polypharmacology and drug discovery. 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