Using Machine Learning To Make Drug Discovery Better

Using Machine Learning To Make Drug Discovery Better

Using Machine Learning To Make Drug Discovery Better
New drugs typically take 12-14 years to make it to market, with a 2014 report finding that the average cost of getting a new drug to market had ballooned to a whopping $2.6 billion.

It’s a topic I’ve covered before, with a study published earlier this year highlighting how automation could be used to reduce the cost of drug discovery by approximately 70%.

It’s an approach that a number of companies are taking to market. For instance, London based start-up Benevolent.AI utilizes complex AI to look for patterns in the scientific literature.

They have already managed to identify two potential drug targets for Alzheimer’s that has already attracted the attention of pharmaceutical companies.

A nice example of what could be 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.

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The approach is nice, because rather than looking solely at the molecular structure to test its viability, they look instead at a number of features related to how the drug binds to molecules.

“We looked more broadly at drug molecule features that drug developers thought were unimportant in predicting drug safety in the past. Then we let the data speak for itself,” the authors say.

It’s an approach known as PrOCTOR, and it took its inspiration from the Moneyball method popularized in baseball.

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