Machine Learning Approach to Antibiotic Discovery: Difference between revisions
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Revision as of 21:20, 24 April 2020
Summary
By Reed Crocker
Biology 238: Microbiology
Spring 2020
Antibiotics are essential for the modern practice of medicine. However, the rise of antibiotic-resistant bacteria present new challenges to paradigm in medical treatment. Discovery and development of novel antibiotics is not meeting the pace of microbial resistance. The arms race against pathogenic bacteria has prompted significant research into new modes of antibiotic discovery. One rapidly expanding sector of this research uses a high throughput, machine learning approach to predict candidate antibiotics in silico.
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Traditional Discovery Methods
Research about how the gradient control enables higher tolerance.
Advantages of Machine Learning in Antibiotic Discovery
High throughput, less costly, quicker.
Future
Where this research might continue to.
Section 4
Conclusion
References
Authored for BIOL 238 Microbiology, taught by Joan Slonczewski, 2018, Kenyon College.