Literature DB >> 30916462

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

Antonio Peón1,2,3,4, Hongjian Li5,6, Ghita Ghislat7, Kwong-Sak Leung8, Man-Hon Wong8, Gang Lu6, Pedro J Ballester1,2,3,4.   

Abstract

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.
© 2019 John Wiley & Sons A/S.

Keywords:  polypharmacology prediction; target deconvolution; target fishing; target prediction; webserver

Year:  2019        PMID: 30916462     DOI: 10.1111/cbdd.13516

Source DB:  PubMed          Journal:  Chem Biol Drug Des        ISSN: 1747-0277            Impact factor:   2.817


  6 in total

1.  Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope.

Authors:  Neann Mathai; Johannes Kirchmair
Journal:  Int J Mol Sci       Date:  2020-05-19       Impact factor: 5.923

2.  Bioactivity and Molecular Docking Studies of Derivatives from Cinnamic and Benzoic Acids.

Authors:  Yunierkis Perez-Castillo; Tamires C Lima; Alana R Ferreira; Cecília R Silva; Rosana S Campos; João B A Neto; Hemerson I F Magalhães; Bruno C Cavalcanti; Hélio V N Júnior; Damião P de Sousa
Journal:  Biomed Res Int       Date:  2020-05-21       Impact factor: 3.411

3.  Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit.

Authors:  Ghita Ghislat; Taufiq Rahman; Pedro J Ballester
Journal:  Biomolecules       Date:  2020-11-19

4.  Identification of potential targets of the curcumin analog CCA-1.1 for glioblastoma treatment : integrated computational analysis and in vitro study.

Authors:  Adam Hermawan; Febri Wulandari; Naufa Hanif; Rohmad Yudi Utomo; Riris Istighfari Jenie; Muthi Ikawati; Ahmad Syauqy Tafrihani
Journal:  Sci Rep       Date:  2022-08-17       Impact factor: 4.996

5.  SuperPred 3.0: drug classification and target prediction-a machine learning approach.

Authors:  Kathleen Gallo; Andrean Goede; Robert Preissner; Bjoern-Oliver Gohlke
Journal:  Nucleic Acids Res       Date:  2022-05-07       Impact factor: 19.160

6.  In silico molecular target prediction unveils mebendazole as a potent MAPK14 inhibitor.

Authors:  Jeremy Ariey-Bonnet; Kendall Carrasco; Marion Le Grand; Laurent Hoffer; Stéphane Betzi; Mikael Feracci; Philipp Tsvetkov; Francois Devred; Yves Collette; Xavier Morelli; Pedro Ballester; Eddy Pasquier
Journal:  Mol Oncol       Date:  2020-10-18       Impact factor: 6.603

  6 in total

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