ArticleInternational journal of immunopathology and pharmacology
Revolutionizing antiretroviral therapy for human immunodeficiency virus/AIDS: A computational approach using molecular docking, virtual screening, and 3D pharmacophore building to address therapeutic failure and propose highly effective candidates.
Article in International journal of immunopathology and pharmacology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Simultaneous Quantification of Bictegravir and Emtricitabine Using a Simple and Economical Liquid Chromatography-Ultraviolet Method for Therapeutic Drug Monitoring in Patients With Human Immunodeficiency Virus.Therapeutic drug monitoring · 2026Article
- Syk protein inhibitors treatment for the allergic symptoms associated with hyper immunoglobulin E syndromes: A focused on a computational approach.International journal of immunopathology and pharmacologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIn the context of human immunodeficiency virus (HIV) treatment, the emergence of therapeutic failures with existing antiretroviral drugs presents a significant challenge. This study aims to employ advanced molecular modeling techniques to identify potential alternatives to current antiretroviral agents.
methodsThe study focuses on three essential classes of antiretroviral drugs: nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and protease inhibitors (PIs). Computational analyses were performed on a database of 3,343,652 chemical molecules to evaluate their binding affinities, pharmacokinetic properties, and interactions with viral reverse transcriptase and protease enzymes. Molecular docking, virtual screening, and 3D pharmacophore modeling were utilized to identify promising candidates.
resultsMolecular docking revealed compounds with high binding energies and strong interactions at the active sites of target enzymes. Virtual screening narrowed down potential candidates with favorable pharmacological profiles. 3D pharmacophore modeling identified crucial structural features for effective binding. Overall, two molecules for class 1, 7 molecules for class 2, and 2 molecules for class 3 were selected. These compounds exhibited robust binding affinities, interactions with target enzymes, and improved pharmacokinetic properties, showing promise for more effective HIV treatments in cases of therapeutic failures.
conclusionThe combination of molecular docking, virtual screening, and 3D pharmacophore modeling yielded lead compounds that hold potential for addressing HIV therapeutic failures. Further experimental investigations are essential to validate the efficacy and safety of these compounds, with the ultimate goal of advancing toward clinical applications in HIV management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.