Anti-flavi: A Web Platform to Predict Inhibitors of Using QSAR and Peptidomimetic Approaches.

Rajput, Akanksha and Kumar, Manoj (2018) Anti-flavi: A Web Platform to Predict Inhibitors of Using QSAR and Peptidomimetic Approaches. Frontiers in microbiology, 9. p. 3121. ISSN 1664-302X

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are arboviruses, which comprises more than 70 viruses, covering broad geographic ranges, and responsible for significant mortality and morbidity globally. Due to the lack of efficient inhibitors targeting flaviviruses, the designing of novel and efficient anti-flavi agents is an important problem. Therefore, in the current study, we have developed a dedicated prediction algorithm , to identify inhibition ability of chemicals and peptides against flaviviruses through quantitative structure-activity relationship based method. We extracted the non-redundant 2168 chemicals and 117 peptides from ChEMBL and AVPpred databases, respectively, with reported IC values. The regression based model developed on training/testing datasets of 1952 chemicals and 105 peptides displayed the Pearson's correlation coefficient (PCC) of 0.87, 0.84, and 0.87, 0.83 using support vector machine and random forest techniques correspondingly. We also explored the peptidomimetics approach, in which the most contributing descriptors of peptides were used to identify chemicals having anti-flavi potential. Conversely, the selected descriptors of chemicals performed well to predict anti-flavi peptides. Moreover, the developed model proved to be highly robust while checked through various approaches like independent validation and decoy datasets. We hope that our web server would prove a useful tool to predict and design the efficient anti-flavi agents. The anti-flavi webserver is freely available at URL

Item Type: Article
Uncontrolled Keywords: QSAR; flaviviruses; inhibitor; machine learning techniques; peptidomimetics; prediction algorithm; random forest; support vector machine
Subjects: Q Science > QR Microbiology
Depositing User: Dr. K.P.S.Sengar
Date Deposited: 19 Mar 2019 15:21
Last Modified: 19 Mar 2019 15:21

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