Machine learning algorithm helps in the search for new drugs

February 11, 2019, University of Cambridge
Credit: CC0 Public Domain

Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard, which could accelerate the process of developing new treatments for disease.

The researchers, led by the University of Cambridge, used their algorithm to identify four new 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.

A key problem in drug discovery is predicting whether a molecule will activate a particular physiological process. It's possible to build a by searching for chemical patterns shared among molecules known to activate that process, but the data to build these models is limited because experiments are costly and it is unclear which chemical patterns are statistically significant.

"Machine learning has made significant progress in areas such as computer vision where data is abundant," said Dr. Alpha Lee from Cambridge's Cavendish Laboratory, and the study's lead author. "The next frontier is scientific applications such as , 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.

Importantly, the algorithm looks at both molecules known to be active and molecules known to be inactive, and learns to recognise which parts of the molecules are important for drug action and which parts are not. A mathematical principle known as gives predictions about the of a random and noisy dataset, which is then compared against the statistics of chemical features of active/inactive molecules to distil which chemical patterns are truly important for binding as opposed to arising simply by chance.

This methodology allows the researchers to fish out important 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.

The researchers built a model starting with 222 , and were able to computationally screen an additional six million molecules. From this, the researchers purchased and screened the 100 most relevant molecules. From these, they identified four new molecules that activate the CHRM1 receptor, a protein that may be relevant for Alzheimer's disease and schizophrenia.

"The ability to fish out four active molecules from six million is like finding a needle in a haystack," said Lee. "A head-to-head comparison shows that our algorithm is twice as efficient as the industry standard."

Making 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 methodology to materials discovery.

The research was supported by the Winton Programme for the Physics of Sustainability.

Explore further: Treatment for obesity and fatty liver disease may be in reach

More information: Alpha A. Lee el al., "Ligand biological activity predicted by cleaning positive and negative chemical correlations," PNAS (2019). www.pnas.org/cgi/doi/10.1073/pnas.1810847116

Related Stories

Artificial intelligence system designs drugs from scratch

July 31, 2018

An artificial-intelligence approach created at the University of North Carolina at Chapel Hill Eshelman School of Pharmacy can teach itself to design new drug molecules from scratch and has the potential to dramatically accelerate ...

Recommended for you

Where is the universe hiding its missing mass?

February 15, 2019

Astronomers have spent decades looking for something that sounds like it would be hard to miss: about a third of the "normal" matter in the Universe. New results from NASA's Chandra X-ray Observatory may have helped them ...

What rising seas mean for local economies

February 15, 2019

Impacts from climate change are not always easy to see. But for many local businesses in coastal communities across the United States, the evidence is right outside their doors—or in their parking lots.

0 comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.