ArticleBiomolecules2023
AI-Aided Search for New HIV-1 Protease Ligands.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
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.
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Who cites it
11 citing papers in PubMed, 9 citations in OpenAlex.
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Is the molecular microenvironment of the latent HIV reservoir predictable using deep learning approaches?Journal of virology · 2026Review
- Harnessing machine learning models to repurpose drugs targeting HIV-1 integrase, protease, and reverse transcriptase.Scientific reports · 2026Article
- Antibody-drug conjugates for infectious and neglected tropical diseases: chemical design principles, target biology, and translational challenges.Frontiers in chemistry · 2026Review
- Article
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
- HIV-1 and Artificial Intelligence: From Molecular Insight to Population Impact.Journal of AIDS and HIV treatment · 2025Article
- AI applications in HIV research: advances and future directions.Frontiers in microbiology · 2025Review
- Review
- Synthesis and characterization of gold(I) thiolate derivatives and bimetallic complexes for HIV inhibition.Frontiers in chemistry · 2024Article
- Introducing the Automated Ligand Searcher.Journal of chemical information and modeling · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The availability of drugs capable of blocking the replication of microorganisms has been one of the greatest triumphs in the history of medicine, but the emergence of an ever-increasing number of resistant strains poses a serious problem for the treatment of infectious diseases. The search for new potential ligands for proteins involved in the life cycle of pathogens is, therefore, an extremely important research field today. In this work, we have considered the HIV-1 protease, one of the main targets for AIDS therapy. Several drugs are used today in clinical practice whose mechanism of action is based on the inhibition of this enzyme, but after years of use, even these molecules are beginning to be interested by resistance phenomena. We used a simple artificial intelligence system for the initial screening of a data set of potential ligands. These results were validated by docking and molecular dynamics, leading to the identification of a potential new ligand of the enzyme which does not belong to any known class of HIV-1 protease inhibitors. The computational protocol used in this work is simple and does not require large computational power. Furthermore, the availability of a large number of structural information on viral proteins and the presence of numerous experimental data on their ligands, with which it is possible to compare the results obtained with computational methods, make this research field the ideal terrain for the application of these new computational techniques.
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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.