Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard.
“Machine learning has made significant progress in areas such as computer vision where data is abundant,” said Winton Advanced Research Fellow Dr Alpha Lee from Cambridge’s Cavendish Laboratory, and the study’s lead author. “The next frontier is scientific applications such as drug discovery, where the amount of data is relatively limited but we do have physical insights about the problem, and the question becomes how to marry data with fundamental chemistry and physics.”
The algorithm developed by Lee and his colleagues, in collaboration with biopharmaceutical company Pfizer, uses mathematics to separate pharmacologically relevant chemical patterns from irrelevant ones. The researchers used their algorithm to identify four new molecules that activate a protein which is thought to be relevant for symptoms of Alzheimer’s disease and schizophrenia. The results are reported in the journal PNAS
This methodology allows the researchers to fish out important chemical patterns not only from molecules that are active but also from molecules that are inactive – in other words, failed experiments can now be exploited with this technique.
Making complex organic molecules is a significant challenge in chemistry, and potential drugs abound in the space of yet-unmakeable molecules. The Cambridge researchers are currently developing algorithms that predict ways to synthesise complex organic molecules, as well as extending the machine learning methodology to materials discovery.
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