New system provides early warning of cellular stress

I’ve written before about some fascinating projects using machine learning to test the potential toxicity of medicines.  A good example of what’s possible is provided by a recent study published in Cell Chemical Biology.  The study reveals a big data based approach to detecting toxic side effects that would prohibit a drug from being used on humans before it gets to the expensive clinical trial stage.

Or you’ve got the recent Stanford University study, which highlights the potential of AI, even with relatively small amounts of data to play with.  The team used a new kind of deep learning known as one-shot learning that can do its stuff with relatively few data points.

“We’re trying to use machine learning, especially deep learning, for the early stage of drug design,” the team say. “The issue is, once you have thousands of examples in drug design, you probably already have a successful drug.

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