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Mycobacterium tuberculosis

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Lead Informed Artificial Intelligence Mining of Antitubercular Host Defense Peptides.

Biomacromolecules
Identifying host defense peptides (HDPs) that are effective against drug-resistant infections is challenging due to their vast sequence space. Artificial intelligence (AI)-guided design can accelerate HDP discovery, but it traditionally requires larg...

Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.

Briefings in bioinformatics
Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discove...

Small Molecule Inhibitors of Topoisomerase I Identified by Machine Learning and In Vitro Assays.

International journal of molecular sciences
Tuberculosis (TB) caused by is a leading infectious cause of death globally. The treatment of patients becomes much more difficult for the increasingly common multi-drug resistant TB. Topoisomerase I is essential for the viability of and has been v...

Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors.

Expert opinion on drug discovery
BACKGROUND: Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant ro...

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

BMC infectious diseases
BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predi...

Artificial Intuition and accelerating the process of antimicrobial drug discovery.

Computers in biology and medicine
New drug development is a very challenging, expensive, and usually time-consuming process. This issue is very important with regard to antimicrobials, which are affected by the global issue of the development and spread of resistance. This framework ...

Deep learning-driven bacterial cytological profiling to determine antimicrobial mechanisms in .

Proceedings of the National Academy of Sciences of the United States of America
Tuberculosis (TB), caused by , remains a significant global health threat, affecting an estimated 10.6 million people in 2022. The emergence of multidrug resistant and extensively drug resistant strains necessitates the development of novel and effec...

Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.

Nature communications
New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Model (GAM) that accurately identifies genetic variants linked to drug resistance and mitigates false-po...

A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.

Scientific reports
Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains a significant global public health threat. HIV co-infection significantly increases the risk of active TB recurrence and prolongs medical treatment for tuberc...