A novel QSAR model to predict epidermial growth factor inhibitors

Over-expression of the Epidermial Growth Factor Receptor (EGFR) is usually present in more than 90% of the Head and Neck Squamous Cell Cancer (HNSCC), due to this the selection of more selective and powefull inhibitors is a major point to threat this type of cancer. In fact, has been demonstrated that this over-expression is responsible of a more aggressive disease, increased resistance to chemotherapy and radiotherapy, increased metastasis, inhibition of apoptosis, promotion of neoplastic angiogenesis, and, finally, poor prognosis and decreased survival. Computational methods are a major tool while looking for new EGFR inhibitors since should help researchers selecting new and enhanced inhibitors in this area. In this contest, Quantitative structure activity relationship (QSAR) is one of the most and widely used computational technique to select new EGFR inhibitors. Here we will present a new QSAR approach aimed at the prediction of new EGFR inhibitors drugs using 1D molecular descriptors.