Monte-Carlo method-based QSAR model to discover phytochemical urease inhibitors using SMILES and GRAPH descriptors

Abstract

Urease inhibitors are known to play a vital role in the field of medicine as well as agriculture. Special attention is attributed to the development of novel urease inhibitors with a view to treat the Helicobacter pylori infection. Amongst a number of urease inhibitors, a large number of molecules fail in vivo and in clinical trials due to their hydrolytic instability and toxicity profile. The search for potential inhibitors may require screening of large and diverse databases of small molecules and to design novel molecules. 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. Communicated by Ramaswamy H. Sarma