MolTarPred: A web tool for comprehensive target prediction with reliability estimation
Publication in refereed journal


摘要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 (, 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.
著者Antonio Peón, Hongjian Li, Ghita Ghislat, Kwong‐Sak Leung, Man‐Hon Wong, Gang Lu, Pedro J. Balleste
期刊名稱Chemical Biology and Drug Design
頁次1390 - 1401
關鍵詞polypharmacology prediction, target deconvolution, target fishing, target prediction, webserver

上次更新時間 2020-23-10 於 00:09