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Written by Laura McCoubrey

More than ever, we’re learning how important the microbiome is for human health. New research papers outlining the relationship between microbiota and disease are currently emerging daily. The past several years have highlighted how microbiome imbalances are associated with the onset of metabolic syndrome, gastrointestinal disease, neurological disorders, and even reproductive complications [1-4]. For this reason, the human microbiome is becoming increasingly considered as a therapeutic target.

The development of new drugs is notoriously a wasteful process. Whereas thousands or millions of investigational molecules can be optioned for development, the vast majority of these will fail in the in vivo, pre-clinical, or clinical phases. Understandably, this leads to loss of time, money, and resources. A key technology being increasingly employed to streamline the discovery and development of therapeutics is machine learning. Machine learning is a form of artificial intelligence, which solves problems more quickly and more accurately than humans, by finding patterns in large datasets. With the rapidly growing knowledge in microbiome medicine, machine learning is an ideal tool to make the most of the big data.

In a recent review, we have analysed and outlined how machine learning can be best applied for the development of new therapeutics targeted at the microbiome. We explore how machine learning can be used in therapeutic discovery, formulation, and both pre-clinical and clinical assessment: https://doi.org/10.1080/19490976.2021.1872323. Machine learning has the power to streamline the development of novel microbiome medicines; reducing associated costs, increasing clinical performance; and ultimately increasing the number of effective treatments entering the market.


  1. Barlow, G.M., A. Yu, and R. Mathur, Role of the Gut Microbiome in Obesity and Diabetes Mellitus. Nutr Clin Pract, 2015. 30(6): p. 787-97.
  2. Jostins, L., et al., Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature, 2012. 491(7422): p. 119-24.
  3. Cryan, J.F., et al., The gut microbiome in neurological disorders. The Lancet Neurology, 2020. 19(2): p. 179-194.
  4. Chang, D.H., et al., Vaginal microbiota profiles of native Korean women and associations with high-risk pregnancy. Journal of Microbiology and Biotechnology, 2020. 30(2): p. 248-258.