Monte-Carlo Method Based QSAR Model to Discover Phytochemical Urease Inhibitors Using SMILES and GRAPH Descriptors

14 September 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

We developed a Monte-Carlo method based
QSAR model to predict urease inhibiting potency of molecules using SMILES and GRAPH
descriptors on an existing diverse database of urease inhibitors. The QSAR model satisfies all
the statistical parameters required for acceptance as a good model. The model is applied to
identify urease inhibitors among the wide range of compounds in the phytochemical database,
NPACT, as a test case. We combine the ligand-based and structure-based drug discovery
methods to improve the accuracy of the prediction. The method predicts pIC50 and estimates
docking score of compounds in the database. The method may be applied to any other database
or compounds designed in silico to discover novel drugs targeting urease.

Keywords

QSAR Modeling
Monte-Carlo Method
Urease inhibitors
Phytochemicals
Drug designing

Supplementary materials

Title
Description
Actions
Title
Data Sheet S1-UreaseInhDB436pic50
Description
Actions
Title
Fig.S1 Ligand Interaction
Description
Actions
Title
Fig.S2 Ligand Inophyllum
Description
Actions
Title
TableS1 FinallyAceeptedFAF
Description
Actions
Title
TableS2 qed table
Description
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.