Deep learning-based prediction of chemical accumulation in a pathogenic mycobacterium
2024
Predicting Drug Accumulation in Mycobacterium abscessus Using Deep Learning
Sample size: 1528
publication
Evidence: high
Author Information
Author(s): Sullivan Mark R., Rubin Eric J.
Primary Institution: Cold Spring Harbor Laboratory
Hypothesis
Can deep learning models accurately predict drug accumulation in Mycobacterium abscessus?
Conclusion
Deep learning models can effectively predict drug accumulation in Mycobacterium abscessus, improving the evaluation of antibiotic candidates.
Supporting Evidence
- The study measured the accumulation of 1528 approved drugs in Mycobacterium abscessus.
- Simple chemical properties were found to be ineffective in predicting drug accumulation.
- Deep learning models were trained to predict drug accumulation with high accuracy.
Takeaway
Scientists used computers to help figure out which medicines can get into a tough germ called Mycobacterium abscessus, which helps in making better antibiotics.
Methodology
Liquid chromatography-mass spectrometry was used to measure drug accumulation.
Digital Object Identifier (DOI)
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