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

ABSTRACT: 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

SUBMITTER: Mahendra FR 

PROVIDER: S-EPMC12727953 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature