ArticleFrontiers in pharmacology2025
Optimisation of peptides targeting reverse transcriptase HIV-1 using QSAR, machine learning, and computational approaches.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Efficacy of tannins as silage additives to inhibit deamination process during ensiling: a meta-analysis andVeterinary and animal science · 2026Article
- Anti-HIV Potential ofViruses · 2026Article
- Computational discovery of fenugreek-paitan-turmeric (FPT) bioactive compounds targeting SGLT2 and DPP-4 for glucose homeostasis regulation.Journal of Taibah University Medical Sciences · 2026Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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