In-silico screening of database for finding potential sweet molecules: A combined data and structure based modeling approach

Abstract

In this study, we present a framework comprises of several independent modules which are built upon data based (structure activity relationship and classification model) and structure (molecular docking) based for identifying possible sweeteners from a vast database of natural molecules. A large database, Universal Natural Products Database (UNPD) consisting of 213,210 compounds was screened using the developed framework. At first, 10,184 molecules structurally similar to the known sweeteners were identified in the database. Further, 1924 molecules from these screened molecules were classified as sweet molecules. The shortlisted 1354 molecules were subjected to ADMET analysis. Finally, 60 molecules were arrived at with no toxicity and acceptable oral bioavailability as potential sweetener candidates. Further, molecular docking of these molecules on sweet taste receptor performed to obtain their binding energy, binding sites and correlation with sweetness index. The developed framework offers a convenient route for fast screening of molecules prior to synthesis and testing.

Keywords: Classification model; Molecular docking; Similarity analysis; Structure activity relationship models.