Revolutionizing antiretroviral therapy for human immunodeficiency virus/AIDS: A computational approach using molecular docking, virtual screening, and 3D pharmacophore building to address therapeutic failure and propose highly effective candidates

Abstract

Objectives: In the context of human immunodeficiency virus (HIV) treatment, the emergence of therapeutic failures with existing antiretroviral drugs presents a significant challenge. This study aims to employ advanced molecular modeling techniques to identify potential alternatives to current antiretroviral agents.

Methods: The study focuses on three essential classes of antiretroviral drugs: nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and protease inhibitors (PIs). Computational analyses were performed on a database of 3,343,652 chemical molecules to evaluate their binding affinities, pharmacokinetic properties, and interactions with viral reverse transcriptase and protease enzymes. Molecular docking, virtual screening, and 3D pharmacophore modeling were utilized to identify promising candidates.

Results: Molecular docking revealed compounds with high binding energies and strong interactions at the active sites of target enzymes. Virtual screening narrowed down potential candidates with favorable pharmacological profiles. 3D pharmacophore modeling identified crucial structural features for effective binding. Overall, two molecules for class 1, 7 molecules for class 2, and 2 molecules for class 3 were selected. These compounds exhibited robust binding affinities, interactions with target enzymes, and improved pharmacokinetic properties, showing promise for more effective HIV treatments in cases of therapeutic failures.

Conclusion: The combination of molecular docking, virtual screening, and 3D pharmacophore modeling yielded lead compounds that hold potential for addressing HIV therapeutic failures. Further experimental investigations are essential to validate the efficacy and safety of these compounds, with the ultimate goal of advancing toward clinical applications in HIV management.

Keywords: antiretroviral; computational screening; human immunodeficiency virus-failure of treatment; pharmacophore model.

Figure 1.

3D pharmacophoric map of NRTI class ligands. (a), a simplified view of the model with aligned ligands. (b), a simplified view without aligned ligands.

Figure 2.

3D pharmacophoric map of NNRTI class ligands. (a), a simplified view of the model with aligned ligands. (b), a simplified view without aligned ligands.

Figure 3.

3D pharmacophoric map of PI class ligands. (a), a simplified view of the model with aligned ligands. (b), a simplified view without aligned ligands.

Figure 4.

General process of the virtual screening of the three therapeutic classes; NRTI (a), NNRTI (b) and PI (c).