AIMC Topic: Drug Resistance, Viral

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Predicting the Impact of Drug Resistance Mutations on Inhibitor Potency with Molecular Dynamics and Machine Learning.

The journal of physical chemistry. B
Many enzymes are vital drug targets in diseases such as cancer and pathogenic infections; however, mutations can drastically disrupt inhibitor binding to confer resistance. Resistance mutations primarily occur at the inhibitor binding site, but accom...

Prevalence and factors associated with HIV drug resistance among adult persons living with HIV/AIDS in nine countries of Sub-Saharan Africa using population-based HIV impact assessments: 2015-2019.

BMC public health
INTRODUCTION: HIV drug resistance (HIVDR) remains a significant challenge in sub-Saharan Africa (SSA) due to limited effective Treatment and healthcare resources vary. Using the first widely available HIVDR surveillance data in SSA, we calculated the...

Benchmarking Machine Learning Models for HIV-1 Protease Inhibitor Resistance Prediction: Impact of Data Set Construction and Feature Representation.

Journal of chemical information and modeling
The rapid emergence of drug resistance in viral infections represents a significant global health challenge, threatening the efficacy of treatments for multiple diseases. Machine learning models have emerged as valuable tools for predicting antiviral...

Enhancing the understandings on SARS-CoV-2 main protease (M) mutants from molecular dynamics and machine learning.

International journal of biological macromolecules
While star drugs like Paxlovid have shown remarkable performance in combating SARS-CoV-2, we still face serious challenges such as viral mutants and resistance. In this study, we employ a computational framework combining molecular dynamics (MD) simu...

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
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 ...

Using machine learning and big data to explore the drug resistance landscape in HIV.

PLoS computational biology
Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive...

Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.

Molecular diversity
Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can le...

Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning.

Journal of chemical theory and computation
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...

Evolution of drug resistance in HIV protease.

BMC bioinformatics
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...