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HIV Protease

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Characterization of low level viraemia in HIV-infected patients receiving boosted protease inhibitor-based antiretroviral regimens.

HIV research & clinical practice 32000615
To understand the pathogenesis of low level viraemia (LLV) in HIV-infected patients on boosted protease inhibitors (PI/b), we enrolled 34 subjects with a median HIV-RNA 79 copies/mL and followed them for 15 months. Samples for next generation sequenc...
Adult Anti-Retroviral Agents Antiretroviral Therapy, Highly Active Drug Resistance, Multiple, Viral Female gag Gene Products, Human Immunodeficiency Virus High-Throughput Nucleotide Sequencing HIV Infections HIV Protease HIV Protease Inhibitors HIV-1 Humans Male Middle Aged Mutation United Kingdom Viral Load Viremia
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Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease.

Combinatorial chemistry & high throughput screening 29165067
BACKGROUND: One key step in the development of inhibitors for an enzyme is the application of computational methodologies to predict protein-ligand interactions. The abundance of structural and ligand-binding information for HIV-1 protease opens up t...
Drug Evaluation, Preclinical HIV Protease HIV Protease Inhibitors Ligands Machine Learning Molecular Docking Simulation
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Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks.

BMC bioinformatics 28810826
BACKGROUND: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of ...
Databases, Factual Drug Resistance, Viral HIV Infections HIV Protease HIV Protease Inhibitors HIV Reverse Transcriptase HIV-1 Humans Neural Networks, Computer Reverse Transcriptase Inhibitors
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Machine Learning to Predict Binding Affinity.

Methods in molecular biology (Clifton, N.J.) 31452110
Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic ...
Algorithms Cyclin-Dependent Kinase 2 Databases, Protein HIV Protease Humans Ligands Machine Learning Models, Statistical Molecular Docking Simulation Molecular Dynamics Simulation Proteins Software Supervised Machine Learning Web Browser
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Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints.

Journal of chemical information and modeling 31381335
Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learnin...
Drug Design Drug Discovery HIV Infections HIV Protease HIV Protease Inhibitors HIV-1 Humans Ligands Machine Learning Molecular Docking Simulation Protein Binding
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Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation 31877249
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few clo...
Drug Resistance, Viral HIV Protease HIV Protease Inhibitors HIV-1 Humans Hydrogen Bonding Ligands Machine Learning Molecular Dynamics Simulation Monte Carlo Method Mutation Protein Binding Static Electricity
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Evolution of drug resistance in HIV protease.

BMC bioinformatics 33375936
BACKGROUND: Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques...
Drug Resistance, Viral Evolution, Molecular Genotype HIV Infections HIV Protease HIV Protease Inhibitors HIV-1 Humans Machine Learning Phenotype
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Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning.

Journal of chemical theory and computation 33783217
Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations, especially those remote from the active site, alter drug binding to confer resistance are poorly u...
Drug Resistance, Viral HIV Protease HIV Protease Inhibitors Machine Learning Molecular Dynamics Simulation Mutation
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Machine learning-based prediction of bioactivity in HIV-1 protease: insights from electron density analysis.

Future medicinal chemistry 39533796
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 ...
Electrons HIV Protease HIV Protease Inhibitors HIV-1 Humans Hydrogen Bonding Ligands Machine Learning Models, Molecular
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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 39807569
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 ...
HIV Protease HIV Protease Inhibitors HIV-1 Humans Machine Learning Proteolysis Substrate Specificity
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