A QSAR Study of Human Thymidine Phosphorylase Inhibitors with SMILES-Based Descriptors

Abstract

Thymidine phosphorylase (TP) is a multifunctional protein frequently overexpressed in many types of cancer. Considering the interest in new anticancer compounds, a QSAR study was carried out to investigate a set of uracil derivatives described as TP inhibitors. The only molecular descriptors used were derived from SMILES notation. Ordered Predictors Selection (OPS) was used for variable selection and the models were built using the PLS method. The authors validated the internal and external prediction capabilities of the obtained model. The model was also tested using a set of 12 molecules obtained by similarity search in the ZINC database. The results showed that it is possible to describe the variation of biological activity of the selected dataset using only SMILES-derived molecular descriptors, and the obtained model shows potential for use as an aid in the design of new TP inhibitors.