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

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.