ArticleACS omega2025
Designing Potent HIV‑1 Protease Inhibitors Using Machine Learning and QSAR Approaches.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Advancements in development of novel class of HIV protease inhibitors.Annals of medicine · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acquired Immune Deficiency Syndrome (AIDS), caused by Human Immunodeficiency Virus type-1 (HIV-1), remains a global health crisis. Despite advances in antiretroviral therapy, drug resistance, particularly to protease inhibitors, persists as a significant challenge. Darunavir, a second-generation protease inhibitor, has reduced efficacy against resistant HIV-1 variants, underscoring the development of new inhibitors. This study combines machine learning (ML) and quantitative structure-activity relationship (QSAR) models to design potent HIV-1 protease inhibitors using phenol-based and polyphenol-based P2 ligands. QSAR models, including genetic function approximation (GFA), multiple linear regression (MLR), random forest (RF), gradient boosting regressor (GBR), and Extreme Gradient Boosting (XGBoost), were developed to analyze molecular descriptors. GBR exhibited the highest accuracy (
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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.