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AnonTechie writes:

The drugs of tomorrow may be discovered by computers. A proof-of-concept study published December 23 in Cell Systems demonstrates that with the right input of data about infectious yeast, a machine algorithm can learn to identify combinations of existing and previously unknown compounds that can work together as antifungal agents. While the method needs to be perfected, it's a new approach to combat infectious disease with the potential to rapidly identify combinations of agents that might help overcome drug resistance.

Many areas of research now use machine learning to find patterns in complex datasets; for example, in pattern recognition of images on the web or in robotic control systems. "This trend has recently exploded in the biosciences, where increasingly machine learning is used to help researchers to make sense of enormous genome-scale datasets," says co-first author Jan Wildenhain, a systems developer at the University of Edinburgh. "The amount of biological data has simply become too large and complex to be processed by human intuition alone."

The researchers' first attempt at a machine learning algorithm was made with brewer's yeast (S. cerevisiae), because it is the only yeast that has had its genetic network mapped out. So although drug-resistant bacteria are the current prevailing public health concern, the model yeast system provides a larger and more informative dataset for this type of study.

http://phys.org/news/2015-12-chemical-combos-pathogenic-yeast.html

[Abstract]: http://www.sciencedirect.com/science/article/pii/S2405471215002173

Original Submission