Optimisation of peptides targeting reverse transcriptase HIV-1 using QSAR, machine learning, and computational approaches

The emergence of drug resistance and adverse side effects associated with current HIV-1 reverse transcriptase (RT) inhibitors underscores the need for novel therapeutic strategies. Peptide-based drugs offer high specificity and lower toxicity, but their development is challenged by the vast combinatorial space of possible sequences and formulation issues. This study aims to identify potent tripeptide-based inhibitors targeting HIV-1 RT through an integrated computational pipeline combining machine learning, QSAR modeling, and in silico validation techniques. From 2,197 screened tripeptides, three candidates, namely FHW, HFW, and HHW, emerged with superior predicted affinity, stability, and drug-like properties. Among them, FHW exhibited the strongest interaction with HIV-1 RT, with a binding energy of −63.50 kcal/mol using MM/GBSA, outperforming the reference drug Nevirapine. FHW peptide also shows four same residues with Nevirapine, including Leu100, Val106, Tyr181, and Tyr188 by hydrophobic contacts. DFT analysis revealed favorable electronic properties, including a low HOMO-LUMO gap (4.73 eV) and high electrophilicity index (13.60). Based on these findings, the FHW peptide demonstrates the highest electrophilicity index among the four ligands, indicating superior electrophilic character relative to Nevirapine. PerMM simulations further indicated consistently negative energy profiles for FHW during membrane translocation and reflecting favorable interactions with the lipid environment. Molecular dynamics simulations confirmed the structural stability of the FHW–RT complex over a 100 ns trajectory. Collectively, these findings identify FHW as the most promising tripeptide-based RT inhibitor with potential for development into next-generation HIV therapeutics, with HFW and HHW as alternative candidates.