Biological and Medicinal Chemistry

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

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.

Content

Thumbnail image of Manuscript_Smiles_2020Modified.pdf

Supplementary material

Thumbnail image of Data Sheet  S1-UreaseInhDB436pic50.txt
Data Sheet S1-UreaseInhDB436pic50
Thumbnail image of Fig.S1_Ligand_Interaction.pdf
Fig.S1 Ligand Interaction
Thumbnail image of Fig.S2_Ligand_Inophyllum.pdf
Fig.S2 Ligand Inophyllum
Thumbnail image of TableS1_FinallyAceeptedFAF.xls
TableS1 FinallyAceeptedFAF
Thumbnail image of TableS2_qed_table.xls
TableS2 qed table