Structure-Guided Design of Novel Diarylpyrimidine-Based NNRTIs Through a Comprehensive In Silico Approach: 3D-QSAR, ADMET Evaluation, Molecular Docking, and Molecular Dynamics

Background/Objectives: The emergence of drug-resistant HIV-1 strains challenges the long-term efficacy of current antiretroviral therapies. Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are critical in HIV-1 treatment; however, the need for new candidates with improved resistance profiles and pharmacokinetics remains. This study aims to design and evaluate novel NNRTIs targeting both wild-type (WT) and mutant-type (MT) HIV-1 reverse transcriptase (RT) using integrated computational strategies. Methods: We conducted a 3D-QSAR study on 33 naphthyl-diarylpyrimidine derivatives using CoMFA and CoMSIA models. We designed thirty-five novel molecules based on contour map insights. We applied ADMET and drug-likeness filters to prioritize ten candidates. Molecular docking was performed on WT (PDB: 3HVT) and MT (PDB: 4PUO) RT structures. The top candidates underwent 100 ns molecular dynamics (MD) simulations. We analyzed structural stability via RMSD, RMSF, and Rg, while we used SASA and MolSA to assess solvent exposure and surface compactness. Results: The CoMFA and CoMSIA models demonstrated robust predictivity (R2 = 0.979/0.920, Q2 = 0.643/0.546, R2test = 0.747/0.603). P14 and P43 showed higher binding affinities than nevirapine and favorable ADMET profiles. MD simulations confirmed stable binding in WT-RT and adaptive flexibility in MT-RT. SASA and MolSA analysis revealed favorable conformational compaction. Drug-likeness profiles indicated optimal log P, strong hydrogen bonding, and acceptable bioavailability. Conclusions: P14 and P43 demonstrate strong potential as NNRTI leads, combining binding affinity, structural stability, and favorable pharmacokinetics, supporting further experimental development.