AIMC Topic: Drug Resistance, Viral

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Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

Viruses
The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying...

Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation
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...

Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.

Scientific reports
Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity an...

Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations.

Proceedings of the National Academy of Sciences of the United States of America
Biophysical interactions between proteins and peptides are key determinants of molecular recognition specificity landscapes. However, an understanding of how molecular structure and residue-level energetics at protein-peptide interfaces shape these l...

A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.

Journal of chemical information and modeling
HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of the lifespans of HIV-infected patients. Therefore, methods capable of predicting resistance susceptibility in the development of compounds...

Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks.

BMC bioinformatics
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 ...

Paraconsistents artificial neural networks applied to the study of mutational patterns of the F subtype of the viral strains of HIV-1 to antiretroviral therapy.

Anais da Academia Brasileira de Ciencias
The high variability of HIV-1 as well as the lack of efficient repair mechanisms during the stages of viral replication, contribute to the rapid emergence of HIV-1 strains resistant to antiretroviral drugs. The selective pressure exerted by the drug ...

Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks.

Journal of medical systems
Resistance to antiretroviral drugs has been a major obstacle for long-lasting treatment of HIV-infected patients. The development of models to predict drug resistance is recognized as useful for helping the decision of the best therapy for each HIV+ ...

Use of Dried Plasma Spots for HIV-1 Viral Load Determination and Drug Resistance Genotyping in Mexican Patients.

BioMed research international
Monitoring antiretroviral therapy using measurements of viral load (VL) and the genotyping of resistance mutations is not routinely performed in low- to middle-income countries because of the high costs of the commercial assays that are used. The ana...

Influenza virus genotype to phenotype predictions through machine learning: a systematic review.

Emerging microbes & infections
BACKGROUND: There is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in add...