Virtual screening studies on HIV-1 reverse transcriptase inhibitors to design potent leads.

Area of Science:

• Medicinal Chemistry
• Structural Biology
• Computational Drug Discovery

Background:

• HIV-1 reverse transcriptase (RT) is a critical target for antiviral therapy.
• Developing novel and potent inhibitors remains essential to combat HIV/AIDS and overcome drug resistance.

Purpose of the Study:

• To identify novel and potent inhibitors targeting HIV-1 reverse transcriptase (RT).
• To utilize structure-based drug design and computational modeling for lead discovery.

Main Methods:

• Crystal structure of the most active ligand was used to generate a feature-shaped query.
• 3D-QSAR and Pharmacophore models (HypoGen) were developed for molecular alignment and prediction.
• Docking studies were employed to refine and validate potential inhibitor candidates.

Main Results:

• A statistically valid 3D-QSAR model achieved an r(2) of 0.873.
• The optimal Pharmacophore model comprised three features: hydrogen bond acceptor, hydrophobic aliphatic, and ring aromatic.
• The model was validated using known RT inhibitors, and novel inhibitors were proposed.

Conclusions:

• Novel and potential lead molecules targeting HIV-1 reverse transcriptase were identified.
• Structure-based and analog studies provide a foundation for developing new anti-HIV therapeutics.