Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Artificial intelligence in human immunodeficiency virus mutation prediction and drug design: Advancing personalized treatment and prevention

26 Jun 2025

Abstract excerpt

Despite significant advancements in highly active antiretroviral therapy (HAART), Human Immunodeficiency Virus (HIV) remains a global health challenge due to its high mutation rate, drug resistance, and the complexity of treatment optimization. Artificial intelligence (AI) has emerged as a transformative tool in HIV research, offering innovative solutions for predicting viral mutations, optimizing drug discovery...

Topics

Join the communities discussing this publication.

HIV/AIDS drug development and treatment
HIV/AIDS Research and Interventions
HIV Research and Treatment

Source

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Identifiers

PMID: 41550634
DOI: 10.1016/j.pscia.2025.100080

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Artificial intelligence in human immunodeficiency virus mutation prediction and drug design: Advancing personalized treatment and preventionPMID 41550634Read ↗

Bioinformatical assistance of selecting anti-HIV therapies: where do we stand?Semantic proximity Read ↗

Predicting the response to combination antiretroviral therapy: retrospective validation of geno2pheno-THEO on a large clinical database.Semantic proximity Read ↗

The application of artificial neural networks for phenotypic drug resistance prediction: evaluation and comparison with other interpretation systems.Semantic proximity Read ↗

Computer-Aided Optimization of Combined Anti-Retroviral Therapy for HIV: New Drugs, New Drug Targets and Drug Resistance.Semantic proximity Read ↗

Forecasting drug resistant HIV protease evolution.Semantic proximity Read ↗

Role of Artificial Intelligence in Modern Drug Discovery: A Literature OverviewSemantic proximity Read ↗

Real-World Validation of Machine Learning Models for HIV Treatment Adherence Prediction and Care Gap Quantification: A Multi-Country Analysis of 192,732 Clinical RecordsSemantic proximity Read ↗

Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.Semantic proximity Read ↗

HIV-1 drug resistance profiling using amino acid sequence space cartographySemantic proximity Read ↗

Real-World Validation of Machine Learning Models for HIV Treatment Adherence Prediction and Care Gap Quantification: A Multi-Country Analysis of 192,732 Clinical RecordsSemantic proximity Read ↗

Computational models for prediction of response to antiretroviral therapies.Semantic proximity Read ↗

Predicting drug-resistant mutations of HIV protease.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.