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Drug Resistance, Viral

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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+ ...

Tackling the problem of HIV drug resistance.

Postepy biochemii
The virally-encoded HIV-1 protease is an effective target for antiviral drugs, however, treatment for HIV infections is limited by the prevalence of drug resistant viral mutants. In this review, we describe our three-pronged approach to analyze and c...

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

Current HIV research
BACKGROUND: Resistance to antiretroviral drugs is a complex and evolving area with relevant implications in the treatment of human immunodeficiency virus (HIV) infection. Several rules, algorithms and full-fledged computer programs have been develope...

Long-term Virologic Suppression Despite Presence of Resistance-associated Mutations Among Perinatally HIV-infected Youth.

The Pediatric infectious disease journal
BACKGROUND: There is limited information on long-term consequences of continuing combination antiretroviral therapy (cART) consisting of <3 active drugs in treatment-experienced youth with perinatal HIV (PHIV). This study describes the clinical outco...