TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds

Liu, Xian; Gao, Yuan; Peng, Jiantang; Yue, Xu; Wang, Yulan; Zhou, Nannan; Xing, Jing; Luo, Xiaomin; Jiang, Hualiang; Zheng, Mingyue

doi:10.1093/bioinformatics/btv099

Cited by 38 publications

(28 citation statements)

References 7 publications

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“…12–15,27 We would like to use AEs for predicting novel drug–target interactions for drug repurposing and repositioning. We would also like to develop an interactive web portal such as 8,9,28,29 that users can utilize to query, retrieve, and analyze data collected in this database. The database would be searchable by drug, drug target, AE, condition, and/or clinical trial.…”

Section: Discussionmentioning

confidence: 99%

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials

Federer

Yoo

Tan

2016

ASSAY and Drug Development Technologies

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Drug adverse events (AEs) are a major health threat to patients seeking medical treatment and a significant barrier in drug discovery and development. AEs are now required to be submitted during clinical trials and can be extracted from (), a database of clinical studies around the world. By extracting drug and AE information from and structuring it into a database, drug-AEs could be established for future drug development and repositioning. To our knowledge, current AE databases contain mainly U.S. Food and Drug Administration (FDA)-approved drugs. However, our database contains both FDA-approved and experimental compounds extracted from . Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from using a set of python scripts, and then used regular expressions and a drug dictionary to process and structure relevant information into a relational database. We performed data mining and pattern analysis of drug-AEs in our database. Our database can serve as a tool to assist researchers to discover drug-AE relationships for developing, repositioning, and repurposing drugs.

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Section: Discussionmentioning

confidence: 99%

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials

Federer

Yoo

Tan

2016

ASSAY and Drug Development Technologies

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“…At present, many online services are available to search for targets of small-molecule drugs by shape screening. According to whether these services and software programs can directly sort the potential protein targets by probability or not, we classified them into direct target prediction tools, such as SuperPred (Dunkel et al, 2008 ), HitPick (Liu et al, 2013 ), ChemMapper, SEA search server, ReverseScreen3D (Kinnings and Jackson, 2011 ), TarPred (Liu et al, 2015a ), and SwissTargetPrediction, and indirect target prediction tools, such as SwissSimilarity (Zoete et al, 2016 ), ChemProt, TargetHunter (Wang L. et al, 2013 ), CSNAP3D (Lo et al, 2016 ), and ROCS. These categories, respectively, are located on the inside and outside of the outer ring in Figure 3 .…”

Section: Methodsmentioning

confidence: 99%

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

et al. 2018

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This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

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“…SEA (Keiser et al., ) has reported impressive prospective results, but only within the 1,400 targets that this tool considers. TarPred (Liu et al., ), HitPick (Liu, Vogt, Haque, & Campillos, ) and SwissTargetPrediction (Gfeller et al., ) have varying degrees of target coverage, with 533, 1,375 and 2,686 targets, respectively. These are target‐centric methods in that they build a predictive model for each target in order to evaluate whether the molecule in question would hit that target (the molecule is evaluated by each of the built models to generate its set of predicted targets).…”

Section: Introductionmentioning

confidence: 99%

MolTarPred: A web tool for comprehensive target prediction with reliability estimation

Peón

Ghislat

et al. 2019

Chem Biol Drug Des

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Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user‐friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.

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TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds

Abstract: TarPred is available at: http://www.dddc.ac.cn/tarpred.

Cited by 38 publications

References 7 publications

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

MolTarPred: A web tool for comprehensive target prediction with reliability estimation

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