Scientists developing new method for drug discovery using simple models and small data sets
The Organizing Committee of Medicinal Chemistry
Conferences is inviting you to attend Euro Medicinal Chemistry
Congress 2019 in April 01 - 02 | Prague, Czech Republic. The topic of the current year's gathering is "Deliberate
the challenges in the New era of Optimizing Medicinal Chemistry & Drug
Design Research Reforms" which will give a worldwide stage to talk of
present and future of Medicinal Chemistry and research.
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.