Cheminformatics (QSAR) study on HIV-1 protease inhibitors

Quantitative Structure Activity Relationship (QSAR) studies are successfully used in drug design and development. In these studies, the structure of a molecule is described by the physico-chemical parameters (descriptors) and a mathematical model is derived to correlate it with biological activity. Cheminformatics approach on the other hand is used to design, organize, retrieve and disseminate chemical information. In the present study, a novel approach based on Cheminformatics analysis using simple QSAR models was used for the first time to understand the inherent relationships between the HIV protease (HIV-PR) inhibitors and their biological activity. Such studies provide mechanistic insight about inhibitor-protein interactions and help in the design of better inhibitors.; HIV-PR is a viral protein of HIV, which is the causative agent of Acquired immunodeficiency syndrome (AIDS) and its related disorders. Several HIV-PR inhibitors (HIV-PIs) which are derived from peptidic and non-peptidic analogs have been approved by US-FDA. They have reduced the viral load sufficiently but have been associated with poor pharmacokinetics, long-term toxicity, drug resistance, and most importantly mutation. Therefore, there is a need to develop new inhibitors that are less toxic and active against wild type and drug resistant mutant viruses. A Cheminformatics (QSAR) analysis was conducted on several classes of HIV-PIs to understand the property of a potent inhibitor and its mechanism of action.; Chemical data pertaining to structure-activity relationships (SAR) were collected and organized from the literature and QSAR models were derived, Cheminformatics analysis was conducted, and hidden chemical information was retrieved. Results are presented for five different datasets of HIV-PIs, both peptidic and non-peptidic analog. Three different classes of pyranone derivatives (pyranone, cycloalkyl-pyranone and dihydro-pyranone) and cyclic urea derivatives represent non-peptidic analogs and indinavir based derivatives represents peptidic analogs. A molecular modeling (docking) study was conducted on the novel compounds designed in silico based on 2D Cheminformatics (QSAR) analysis to visualize and understand the 3D interaction pattern of these compounds with HIV-PR. A study of the Indinavir based analog was also conducted to define the mutation pattern due to these analogs quantitatively. Finally, large comprehensive datasets for all three classes of pyranone based inhibitors, mutated HIV-PR proteins were prepared, and their descriptor values were calculated. These datasets are useful resources for further research.; The Cheminformatics (QSAR) models were successful in predicting the improved structure of the inhibitor molecule, to quantitatively parameterize the characteristic of inhibitor and the protein (HIV-PR) and to interpret the improved structure in terms of favorable inhibitor-protein interactions.