title: TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database.
abstract: Target identification of the known bioactive compounds and novel synthetic analogs is a very important research field in medicinal chemistry, biochemistry, and pharmacology. It is also a challenging and costly step towards chemical biology and phenotypic screening. In silico identification of potential biological targets for chemical compounds offers an alternative avenue for the exploration of ligand-target interactions and biochemical mechanisms, as well as for investigation of drug repurposing. Computational target fishing mines biologically annotated chemical databases and then maps compound structures into chemogenomical space in order to predict the biological targets. We summarize the recent advances and applications in computational target fishing, such as chemical similarity searching, data mining/machine learning, panel docking, and the bioactivity spectral analysis for target identification. We then described in detail a new web-based target prediction tool, TargetHunter (http://www.cbligand.org/TargetHunter). This web portal implements a novel in silico target prediction algorithm, the Targets Associated with its MOst SImilar Counterparts, by exploring the largest chemogenomical databases, ChEMBL. Prediction accuracy reached 91.1% from the top 3 guesses on a subset of high-potency compounds from the ChEMBL database, which outperformed a published algorithm, multiple-category models. TargetHunter also features an embedded geography tool, BioassayGeoMap, developed to allow the user easily to search for potential collaborators that can experimentally validate the predicted biological target(s) or off target(s). TargetHunter therefore provides a promising alternative to bridge the knowledge gap between biology and chemistry, and significantly boost the productivity of chemogenomics researchers for in silico drug design and discovery.
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy, Computational Chemical Genomics Screening Center, Pittsburgh, PA, USA.
authors.forename: Lirong
authors.initials: L
authors.lastname: Wang
authors.forename: Chao
authors.initials: C
authors.lastname: Ma
authors.forename: Peter
authors.initials: P
authors.lastname: Wipf
authors.forename: Haibin
authors.initials: H
authors.lastname: Liu
authors.forename: Weiwei
authors.initials: W
authors.lastname: Su
authors.forename: Xiang-Qun
authors.initials: XQ
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D019380:Anti-HIV Agents / Q000737:chemistry / Q000494:pharmacology; D000959:Antihypertensive Agents / Q000737:chemistry / Q000494:pharmacology; D000970:Antineoplastic Agents / Q000737:chemistry / Q000494:pharmacology; D001185:Artificial Intelligence; D001572:Benzofurans / Q000737:chemistry / Q000494:pharmacology; D003196:Computer Graphics; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D062126:Databases, Chemical; D055808:Drug Discovery / Q000379:methods*; D058492:Drug Repositioning; D008958:Models, Molecular; D062105:Molecular Docking Simulation; D015394:Molecular Structure; D018727:Muscarinic Antagonists / Q000737:chemistry / Q000494:pharmacology; D011139:Polythiazide / Q000737:chemistry / Q000494:pharmacology; D011759:Pyrrolidines / Q000737:chemistry / Q000494:pharmacology; D015203:Reproducibility of Results; D012984:Software; D013329:Structure-Activity Relationship; D014584:User-Computer Interface
chemical_list: D019380:Anti-HIV Agents; D000959:Antihypertensive Agents; D000970:Antineoplastic Agents; D001572:Benzofurans; D018727:Muscarinic Antagonists; D011759:Pyrrolidines; D011139:Polythiazide; C101207:darifenacin