Computational modeling methods for QSAR studies on HIV-1 integrase inhibitors (2005-2010)

Abstract

The human immunodeficiency virus type 1 (HIV-1) integrase is an emerging target for novel antiviral drugs. Quantitative structure-activity relationship (QSAR) models for HIV-1 integrase inhibitors have been developed to understand the protein-ligand interactions to aid in the design of more effective analogs. This review paper presents a comprehensive overview of the computational modeling methods and results of QSAR models of HIV-1 integrase inhibitors published in 2005-2010. These QSAR models are classified according to the generation of molecular descriptors: 2D-QSAR, 3D-QSAR, and 4D-QSAR. Linear and non-linear modeling methods have been applied to derive these QSAR models, with the majority of the models derived from linear statistical methods such as multiple linear regression and partial least squares. While each of the published QSAR models have provided insight on the distinct chemical features of HIV-1 integrase inhibitors crucial for biological activity, only a few models have been used to propose and synthesize new HIV-1 integrase inhibitors. This study highlights the need for collaboration between computational and experimental chemists to utilize and improve these QSAR models to guide the design of the next generation of HIV-1 integrase inhibitors.