Scientists developing new method for drug discovery using simple models and small data sets

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Drug discovery is the designing of compounds to interact with disease-related proteins. And in many recent development efforts, this process increasingly relies on "big data" and complex "deep learning", requiring the harnessing of supercomputing power. But what if this could be done much more simply, requiring less time and expense?

Now a team of scientists has done just that, developing a method using simple models and small data sets -- but still achieving a high degree of predictive ability. The researchers from Kyoto University, MIT, and ETH Zurich reported their findings 6 March in the journal Future Medicinal Chemistry.

The study demonstrates that large amounts of data generated by testing compound activity on protein groups -- known for roles in cancer and other physiological processes -- could be reduced to a small fraction of the total, which could still accurately explain the full set. The subset required was less than a quarter in most cases, and in some, even less than 10%.

"Drug discovery can fall into a trap of trying tens or hundreds of thousands of compounds against proteins, with 1% or less success rates," continues Brown, emphasizing that the new technique can reduce the number of initial tests to a few thousand, from which point scientists can check just the most promising ones.