Machine Learning Approach to Antibiotic Discovery

From MicrobeWiki, the student-edited microbiology resource
Revision as of 15:45, 14 May 2020 by Crocker1 (talk | contribs)
This is a curated page. Report corrections to Microbewiki.

Summary

Graphical abstract from Stokes et al., 2020. Diagrams the computational workflow of the machine learning approach to antibiotic discovery.

By Reed Crocker
Biology 238: Microbiology
Spring 2020

Antibiotics are essential for the modern practice of medicine. However, the rise of antibiotic-resistant bacteria presents new challenges to this 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.

Traditional Discovery Methods

One of the first experimental platforms for antibiotic discovery is known as the Waksman Platform. First implemented by the Rutgers microbiologist Selman Waksman, the Waksman Platform involved the screening of soil-borne actinomycetes on a field of overlaid bacteria[1]. Actinomycetes are bacteria which commonly produce antibiotics in their soil environment. The development of a zone of inhibition on the plate was the primary way candidate antibiotic-producing microbes were discovered. This technique was successfully used to discover several important antibiotics including streptomycin. While this platform was fundamental to finding many of the world’s antibiotics, it has certain limitations. First, there is a finite number of soil antibiotics. Approaches to sample more diverse soils and bacterial environments have been mostly unsuccessful in producing new classes of antibiotic[2]. Second, the approach is not easily scaled up, meaning the process is slow and laborious. These reasons are some of many to prompt high throughput computational approaches to antibiotic discovery.

Methodology of Machine Learning in Antibiotic Discovery

A paper published in February of 2020 revealed their novel platform for the computational discovery of antibiotics.

Advantages of Machine Learning in Antibiotic Discovery

New platforms for antibiotic discovery have overcome many of the challenges presented by the Waksman Platform. High throughput, less costly, quicker.

Future

Where this research might continue to.

Section 4

Conclusion

References

Valiquette, L., & Laupland, K. B. (2015). Digging for New Solutions. Canadian Journal of Infectious Diseases and Medical Microbiology, 26(6), 289–290. https://doi.org/10.1155/2015/971858



Authored for BIOL 238 Microbiology, taught by Joan Slonczewski, 2018, Kenyon College.