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Antitubercular Agents

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A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries.

Tuberculosis (Edinburgh, Scotland)
Tuberculosis is ranked as the 2nd deadliest disease in the world and is responsible for ten million deaths in 2017. Treatment failure is one of a main reason behind these deaths. Reasons of treatment failure are still unknown and the death rate due t...

iATP: A Sequence Based Method for Identifying Anti-tubercular Peptides.

Medicinal chemistry (Shariqah (United Arab Emirates))
BACKGROUND: Tuberculosis is one of the biggest threats to human health. Recent studies have demonstrated that anti-tubercular peptides are promising candidates for the discovery of new anti-tubercular drugs. Since experimental methods are still labor...

Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images.

PloS one
Tuberculosis is an infectious disease that causes ill health and death in millions of people each year worldwide. Timely diagnosis and treatment is key to full patient recovery. The Microscopic Observed Drug Susceptibility (MODS) is a test to diagnos...

Computational Approaches as Rational Decision Support Systems for Discovering Next-Generation Antitubercular Agents: Mini-Review.

Current computer-aided drug design
Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglected tropical diseases that prevail in tropical and subtropical countries, affecting one billion people every year. Minimal funding and grants for research on th...

Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models.

Informatics for health & social care
Tuberculosis (TB) is a deadly contagious disease and a serious global health problem. It is curable but due to its lengthy treatment process, a patient is likely to leave the treatment incomplete, leading to a more lethal, drug resistant form of dise...