2D-QSAR Modeling of Quinazolinone Derivatives as Angiotensin II Type 1a Receptor Blockers

Abstract

Angiotensin receptor blockers (ARBs) are a group of drugs primarily used in the treatment of cardiovascular disease. Multiple quantitative structural activity relationship (QSAR) models established for prediction of angiotensin II type 1a (AT-1a) receptor blocking activity of quinazolinone derivatives to investigate the structural attributes that have significant correlation with biological activity. The genetic algorithm (GA) approach was used to generate a highly predictive models using easily interpretable Py, OEstate and Padel descriptors. OECD principles have been followed to develop statistically robust QSAR models (R2tr = 0.8055 – 0.8625) with good external predictivity (CCCex = 0.7528-0.8450). The multiple QSAR models successfully identified that increase in surface area of negatively charge carbon atom within four bonds from N atom, presence of tetrazole substituents and sp3 N atoms governs the AT-1a receptor blocking activity. The validated QSAR models of present study might be helpful for evaluation AT-1a receptor blocking activity to identify novel hits.

Multi-Target In-Silico modeling strategies to discover novel angiotensin converting enzyme and neprilysin dual inhibitors

“…The selected compounds dataset was subjected to biological and chemical curation 49 by removing the molecules that lack information such as SMILES, units of activity, and duplicates. Structures were standardised, neutralised, and cleaned and salts were removed to get a dataset of 715 compounds which makes ACE (357) and NEP (358) (Supporting file S2 ) followed by 3D optimization using MMFF94 force-field by OpenBabel 50 , 51 . we have calculated a variety of descriptors, namely, Py-Descriptor (Constitutional, geometric, circular fingerprint, quantum chemical and topological, no.…”