Quantitative structure anti-proliferative activity against HEPG2 and SW1116 relationships in a series of Pyrazine Derivatives
Abstract
Quantitative structure activity relationship (QSAR) methods have been applied in several scientific studies including chemistry, biology and toxicology and drug discovery to predict and classify biological activities of virtual or newly-synthesized compounds. QSAR models can also be used in designing new chemical entities and are now regarded as essential tools in pharmaceutical industries to identify promising hits and generate high quality leads in the early stages of drug discovery. QSAR studies were performed on a series of pyrazine as anti-proliferative agents. A multiple linear regression (MLR) procedure was used to envisage the relationships between molecular descriptors and the activity of pyrazine derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based of the following descriptors: logP, HE, SAG, Pol, qC3 for the anti-proliferative activity against the HEPG2 (human liver cancer cell) and logP, HE, MR, SAG, Vol, qC2, qC5, qC6, LUMO for the anti-proliferative activity against the SW1116 (human colorectal carcinoma cell). To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR models. Copyright © 2017 American Scientific Publishers All rights reserved.