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Tuberculosis Drug Discovery in the Age of Artificial Intelligence
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DOI：10.1101/cshperspect.a041828.png)
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Abstract
En 中文
There is an urgent need to develop additional treatments for tuberculosis (TB) to complement the small panel of approved drugs and to devise shorter treatment regimens. Within the last 20 years, we have seen an increased focus on using cheminformatics-based approaches to understand the properties of Mycobacterium tuberculosis (Mtb) active molecules and machine learning algorithms to subsequently learn from public data sets. We now demonstrate how we have continually used many machine learning approaches that have enabled us to select or synthesize new compounds for testing in vitro to validate our models and to identify new chemical matter. We now put our results into context with studies from other groups to make the case for using machine learning models more widely to aid in finding new Mtb inhibitors. TB research has been slow to adapt to these approaches to increase drug discovery efficiency, but it is better late than never.
Keywords:
MACHINE LEARNING-METHODS
MYCOBACTERIUM-TUBERCULOSIS
BAYESIAN MODELS
SMALL MOLECULES
DATA SETS
TB
TARGET
Journal
IF:
10.1
Papers:
3.0K
Citations:
1.3W
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