Preprint Article Version 1 This version is not peer-reviewed

Anti-Nipah: A QSAR Based Prediction Method to Identify the Inhibitors Against Nipah Virus

Version 1 : Received: 5 October 2018 / Approved: 5 October 2018 / Online: 5 October 2018 (15:04:23 CEST)

How to cite: Rajput, A.; Kumar, A.; Kumar, M. Anti-Nipah: A QSAR Based Prediction Method to Identify the Inhibitors Against Nipah Virus. Preprints 2018, 2018100103 (doi: 10.20944/preprints201810.0103.v1). Rajput, A.; Kumar, A.; Kumar, M. Anti-Nipah: A QSAR Based Prediction Method to Identify the Inhibitors Against Nipah Virus. Preprints 2018, 2018100103 (doi: 10.20944/preprints201810.0103.v1).

Abstract

Nipah virus (NiV) is responsible to cause various outbreaks in Asian countries, with latest from Kerala state of India. Till date there is no drug available despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed “anti-Nipah” web resource, which comprised of a data repository, prediction method, and data visualization modules. The database comprised of 313 (181 unique) inhibitors from different strains and outbreaks of NiV extracted from research articles and patents. However, the quantitative structure–activity relationship (QSAR) based predictors were accomplished using classification approach employing 10-fold cross validation through support vector machine with 120 (68p + 52n) inhibitors. The overall predictor showed the accuracy and Matthew’s correlation coefficient of 88.89% and 0.77 on training/testing dataset respectively. The independent validation dataset also performed equally well. The data visualization modules from chemical clustering and principal component analyses displayed the diversity in the NiV inhibitors. Therefore, our web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly webserver is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/

Subject Areas

Nipah Virus, outbreak, inhibitors, QSAR, database, prediction algorithm

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