An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking

Molecular BioSystems
Naeem ShaikhPrabha Garg

Abstract

Proteochemometric (PCM) methods, which use descriptors of both the interacting species, i.e. drug and the target, are being successfully employed for the prediction of drug-target interactions (DTI). However, unavailability of non-interacting dataset and determining the applicability domain (AD) of model are a main concern in PCM modeling. In the present study, traditional PCM modeling was improved by devising novel methodologies for reliable negative dataset generation and fingerprint based AD analysis. In addition, various types of descriptors and classifiers were evaluated for their performance. The Random Forest and Support Vector Machine models outperformed the other classifiers (accuracies >98% and >89% for 10-fold cross validation and external validation, respectively). The type of protein descriptors had negligible effect on the developed models, encouraging the use of sequence-based descriptors over the structure-based descriptors. To establish the practical utility of built models, targets were predicted for approved anticancer drugs of natural origin. The molecular recognition interactions between the predicted drug-target pair were quantified with the help of a reverse molecular docking approach. The majority of pre...Continue Reading

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Citations

Feb 24, 2017·Journal of Chemical Information and Modeling·Naeem ShaikhPrabha Garg
Jun 7, 2017·Chemical Biology & Drug Design·Sharat ChandraMohammad Imran Siddiqi
Sep 15, 2018·Molecular Informatics·Neha TripathiPrabha Garg
Oct 18, 2018·BMC Bioinformatics·Frédéric CadetPhilippe Charton
May 26, 2021·Journal of Chemical Information and Modeling·Kushal BatraSean Ekins
Sep 2, 2020·Journal of Agricultural and Food Chemistry·Se Eun ParkJae Sue Choi

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