IN SILICO DEVELOPMENT OF A QSAR MODEL FOR ANTI VIRAL COMPOUNDS
Aruna Rajubabu*, D. Krishna Teja, Dr. V. Padmaja, Prof. M. Sumakanth
ABSTRACT
Owing to the rising resistance and side effects associated with current antiviral therapies, there is a pressing need for the development of novel antiviral agents with improved efficacy and safety profiles. In this study, an in silico QSAR (Quantitative Structure–Activity Relationship) model was developed to predict the antiviral activity of chemically diverse compounds. A dataset of molecules with reported Anti-HIV activity was retrived from Pubchem, and their IC?? values were converted to PIC??. The compounds included a wide range of heterocyclic scaffolds known for HIV Integrase inhibitors properties. Molecular descriptors calculation was performed using ChemMaster (1.2 Basic free) software. The QSAR model was built using regression based method to identify key molecular features contributing to Antiviral potency. The model was validated, demonstrating strong predictive reliability. This computational approach offers valuable insights into structure–activity relationships and facilitates the novel Anti- HIV agents with enhanced therapeutic potential.
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