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Related Concept Videos

Retrovirus Life Cycles01:10

Retrovirus Life Cycles

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Retroviruses have a single-stranded RNA genome that undergoes a special form of replication. Once the retrovirus has entered the host cell, an enzyme called reverse transcriptase synthesizes double-stranded DNA from the retroviral RNA genome. This DNA copy of the genome is then integrated into the host’s genome inside the nucleus via an enzyme called integrase. Consequently, the retroviral genome is transcribed into RNA whenever the host’s genome is transcribed, allowing the...
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Viral Mutations00:36

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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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Related Experiment Video

Updated: Jan 7, 2026

Single-cell Quantitation of mRNA and Surface Protein Expression in Simian Immunodeficiency Virus-infected CD4+ T Cells Isolated from Rhesus macaques
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HIV-1 and Artificial Intelligence: From Molecular Insight to Population Impact.

Giovannino Silvestri1,2,3, Aditi Chatterjee1,3

  • 1Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA.

Journal of AIDS and HIV Treatment
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing HIV-1 research by analyzing complex data to predict drug resistance and viral evolution. AI integration with advanced technologies promises to accelerate the path toward durable remission and a cure for HIV.

Keywords:
Antiretroviral therapyArtificial intelligenceGenomicsHIV-1

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Area of Science:

  • Virology
  • Computational Biology
  • Artificial Intelligence

Background:

  • Human Immunodeficiency Virus type 1 (HIV-1) presents significant computational challenges due to its rapid evolution, genetic diversity, and persistent latent reservoirs.
  • Analyzing vast datasets, from viral genomes to clinical information, is crucial for understanding and combating HIV-1.
  • Traditional research methods struggle to keep pace with the complexity of HIV-1.

Purpose of the Study:

  • To highlight the indispensable role of Artificial Intelligence (AI) in advancing HIV-1 research.
  • To explore how AI-driven models address the unique computational challenges posed by HIV-1.
  • To showcase AI's potential in accelerating drug discovery, surveillance, and personalized care for HIV-1.

Main Methods:

  • Application of modern machine-learning and deep-learning architectures.
  • Integration of multi-omics information with AI models.
  • Utilizing AI in chemoinformatics, network analysis, and language modeling.
  • Convergence of AI with organoid technologies, single-cell systems biology, and population informatics.

Main Results:

  • AI enables decoding of viral sequences and prediction of drug resistance and co-receptor usage.
  • AI can simulate viral evolutionary trajectories under therapy and identify molecular determinants of persistence.
  • AI-assisted chemoinformatics shortens drug discovery cycles, enhancing epidemiological surveillance and individualized care.

Conclusions:

  • AI is redefining HIV-1 research from observation to dynamic prediction, offering new avenues for treatment and cure.
  • The integration of AI with emerging technologies is crucial for future breakthroughs in HIV-1 management.
  • Ensuring ethical transparency, algorithmic fairness, and equitable access to AI innovations is paramount for equitable progress toward HIV-1 remission and cure.