AIMC Topic: HIV-1

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HIV OctaScanner: A Machine Learning Approach to Unveil Proteolytic Cleavage Dynamics in HIV-1 Protease Substrates.

Journal of chemical information and modeling Jan 14, 2025
The rise of resistance to antiretroviral drugs due to mutations in human immunodeficiency virus-1 (HIV-1) protease is a major obstacle to effective treatment. These mutations alter the drug-binding pocket of the protease and reduce the drug efficacy ...
Substrate Specificity Humans Machine Learning HIV-1 HIV Protease HIV Protease Inhibitors Proteolysis
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A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jan 10, 2025
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug ...
Neural Networks, Computer HIV Infections Anti-HIV Agents Humans HIV-1 Drug Resistance, Viral Graph Neural Networks Treatment Outcome Anti-Retroviral Agents
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Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.

PLoS computational biology Nov 20, 2024
The ability to predict HIV-1 resistance to broadly neutralizing antibodies (bnAbs) will increase bnAb therapeutic benefits. Machine learning is a powerful approach for such prediction. One challenge is that some HIV-1 subtypes in currently available ...
HIV Antibodies Broadly Neutralizing Antibodies Antibodies, Neutralizing HIV-1 Humans Machine Learning HIV Infections Computational Biology Epitopes
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Machine learning-based prediction of bioactivity in HIV-1 protease: insights from electron density analysis.

Future medicinal chemistry Nov 12, 2024
To develop a model for predicting the biological activity of compounds targeting the HIV-1 protease and to establish factors influencing enzyme inhibition. Machine learning models were built based on a combination of Richard Bader's theory of Atoms ...
HIV Protease Inhibitors Hydrogen Bonding HIV-1 Machine Learning Ligands Humans Models, Molecular HIV Protease Electrons
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Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning.

Marine drugs Nov 5, 2024
The secondary metabolites of seawater and freshwater blue-green algae are a rich natural product pool containing diverse compounds with various functions, including antiviral compounds; however, high-efficiency methods to screen such compounds are la...
Humans Machine Learning HIV-1 Cyanobacteria Antiviral Agents Biological Products Molecular Docking Simulation Drug Evaluation, Preclinical
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Pre-training strategy for antiviral drug screening with low-data graph neural network: A case study in HIV-1 K103N reverse transcriptase.

Journal of computational chemistry Oct 22, 2024
Graph neural networks (GNN) offer an alternative approach to boost the screening effectiveness in drug discovery. However, their efficacy is often hindered by limited datasets. To address this limitation, we introduced a robust GNN training framework...
Neural Networks, Computer Machine Learning Molecular Docking Simulation Humans HIV-1 Reverse Transcriptase Inhibitors HIV Reverse Transcriptase Anti-HIV Agents Molecular Dynamics Simulation Drug Evaluation, Preclinical
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HIV-1 M group subtype classification using deep learning approach.

Computers in biology and medicine Oct 5, 2024
Traditionally, the classification of HIV-1 M group subtypes has depended on statistical methods constrained by sample sizes. Here HIV-1-M-SPBEnv was proposed as the first deep learning-based method for classifying HIV-1 M group subtypes via env gene ...
Humans Neural Networks, Computer Deep Learning HIV Infections HIV-1
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Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.

eLife Sep 9, 2024
Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-...
CD4-Positive T-Lymphocytes Anti-Retroviral Agents HIV-1 Machine Learning Proviruses Female Humans Middle Aged Adult HIV Infections Cross-Sectional Studies DNA, Viral Male
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RAIN: machine learning-based identification for HIV-1 bNAbs.

Nature communications Jun 24, 2024
Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a ...
HIV-1 Cryoelectron Microscopy HIV Envelope Protein gp120 Humans Antibodies, Neutralizing HIV Antibodies Machine Learning Amino Acid Sequence CD4 Antigens HIV Infections
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A deep learning approach to real-time HIV outbreak detection using genetic data.

PLoS computational biology Oct 14, 2022
Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylo...
Europe Phylogeny Humans Deep Learning HIV-1 Disease Outbreaks HIV Infections
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