Xian Liu1, Yuan Gao1, Jianlong Peng1, Yuan Xu1, Yulan Wang1, Nannan Zhou1, Jing Xing1, Xiaomin Luo1, Hualiang Jiang2, Mingyue Zheng1. 1. State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China. 2. State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China, Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA and State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Abstract
MOTIVATION: Discovering the relevant therapeutic targets for drug-like molecules, or their unintended 'off-targets' that predict adverse drug reactions, is a daunting task by experimental approaches alone. There is thus a high demand to develop computational methods capable of detecting these potential interacting targets efficiently. RESULTS: As biologically annotated chemical data are becoming increasingly available, it becomes feasible to explore such existing knowledge to identify potential ligand-target interactions. Here, we introduce an online implementation of a recently published computational model for target prediction, TarPred, based on a reference library containing 533 individual targets with 179 807 active ligands. TarPred accepts interactive graphical input or input in the chemical file format of SMILES. Given a query compound structure, it provides the top ranked 30 interacting targets. For each of them, TarPred not only shows the structures of three most similar ligands that are known to interact with the target but also highlights the disease indications associated with the target. This information is useful for understanding the mechanisms of action and toxicities of active compounds and can provide drug repositioning opportunities. AVAILABILITY AND IMPLEMENTATION: TarPred is available at: http://www.dddc.ac.cn/tarpred.
MOTIVATION: Discovering the relevant therapeutic targets for drug-like molecules, or their unintended 'off-targets' that predict adverse drug reactions, is a daunting task by experimental approaches alone. There is thus a high demand to develop computational methods capable of detecting these potential interacting targets efficiently. RESULTS: As biologically annotated chemical data are becoming increasingly available, it becomes feasible to explore such existing knowledge to identify potential ligand-target interactions. Here, we introduce an online implementation of a recently published computational model for target prediction, TarPred, based on a reference library containing 533 individual targets with 179 807 active ligands. TarPred accepts interactive graphical input or input in the chemical file format of SMILES. Given a query compound structure, it provides the top ranked 30 interacting targets. For each of them, TarPred not only shows the structures of three most similar ligands that are known to interact with the target but also highlights the disease indications associated with the target. This information is useful for understanding the mechanisms of action and toxicities of active compounds and can provide drug repositioning opportunities. AVAILABILITY AND IMPLEMENTATION: TarPred is available at: http://www.dddc.ac.cn/tarpred.