Published Paper Details:

The human immunodeficiency virus (HIV) remains a major global health challenge, with millions of people infected worldwide. While significant progress has been made in HIV treatment over the past few decades, here is still an urgent send for novel therapeutic agents to combat drug resistance and improve patient outcomes. In silico drug design has emerged as a powerful tool in the discovery and development of new anti-HIV drugs. By leveraging computational methods and structural data, researchers rapidly identify promising compounds, optimize their properties and predict their interactions with viral targets. This review provides a comprehensive overview of recent advances in in silico drug design strategies for HIV, including structure bases drug design, ligand-based drug design, and machine learning approaches. We discuss the key viral targets for drug discovery, such as reverse transcriptase, protease, and integrase, and highlight successful case studies of computationally-designed HIV inhibitors that have progressed to clinical trials. We also explore the challenges and future directions in this field, such as the need for more accurate binding affinity prediction tools and the integration of in silico methods with experimental validation. Overall, in silico drug design offers a promising avenue for accelerating the discovery of novel anti-HIV therapeutics and improving the lives of those affected by this devastating virus.