AIMC Topic: Antitubercular Agents
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TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.
BMC genomics
Feb 12, 2024
The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biolog...
Treating drug-resistant tuberculosis in an era of shorter regimens: Insights from rural South Africa.
South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde
Nov 6, 2023
BACKGROUND: Progressive interventions have recently improved programmatic outcomes in drug-resistant tuberculosis (DR-TB) care in South Africa (SA). Amidst these, a shorter regimen was introduced in 2017 with weak evidence, and has shown mixed result...
Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis.
Journal of biomolecular structure & dynamics
May 26, 2023
The rise of antibiotic-resistant Mycobacterium tuberculosis (Mtb) has reduced the availability of medications for tuberculosis therapy, resulting in increased morbidity and mortality globally. Tuberculosis spreads from the lungs to other parts of the...
De novo design of anti-tuberculosis agents using a structure-based deep learning method.
Journal of molecular graphics & modelling
Oct 13, 2022
Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents t...
First-line drug resistance profiling of : a machine learning approach.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 21, 2022
The persistence and emergence of new multi-drug resistant Mycobacterium tuberculosis (M. tb) strains continues to advance the devastating tuberculosis (TB) epidemic. Robust systems are needed to accurately and rapidly perform drug-resistance profilin...
Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.
Scientific reports
Feb 14, 2022
Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic s...
Mycobacterium abscessus drug discovery using machine learning.
Tuberculosis (Edinburgh, Scotland)
Jan 20, 2022
The prevalence of infections by nontuberculous mycobacteria is increasing, having surpassed tuberculosis in the United States and much of the developed world. Nontuberculous mycobacteria occur naturally in the environment and are a significant proble...
GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning.
Genome medicine
Aug 30, 2021
BACKGROUND: Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly...
Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents.
Molecular diversity
Jun 10, 2021
Tuberculosis (TB) is an infectious disease and the leading cause of death globally. The rapidly emerging cases of drug resistance among pathogenic mycobacteria have been a global threat urging the need of new drug discovery and development. However, ...
Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.
Scientific reports
Oct 22, 2020
Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce p...
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