Health

Stanford AI Algorithm Solves Structural Biology Challenges

Stanford researchers develop machine learning methods that accurately predict the 3D shapes of drug targets and other important biological molecules, even when only limited data is available.

Determining the 3D shapes of biological molecules is one of the hardest problems in modern biology and medical discovery. Companies and research institutions often spend millions of dollars to determine a molecular structure – and even such massive efforts are frequently unsuccessful.

Using clever, new machine learning techniques, Stanford University PhD students Stephan Eismann and Raphael Townshend, under the guidance of Ron Dror, associate professor of computer science, have developed an approach that overcomes this problem by predicting accurate structures computationally.

https://vocus.cc/article/612e3a43fd89780001d55508

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https://vocus.cc/article/612e39f7fd89780001d55398

Most notably, their approach succeeds even when learning from only a few known structures, making it applicable to the types of molecules whose structures are most difficult to determine experimentally.

Their work is demonstrated in two papers detailing applications for RNA molecules and multi-protein complexes, published in Science on August 27, 2021, and in Proteins in December 2020, respectively. The paper in Science is a collaboration with the Stanford laboratory of Rhiju Das, associate professor of biochemistry.

“Structural biology, which is the study of the shapes of molecules, has this mantra that structure determines function,” said Townshend.

The algorithm designed by the researchers predicts accurate molecular structures and, in doing so, can allow scientists to explain how different molecules work, with applications ranging from fundamental biological research to informed drug design practices.

“Proteins are molecular machines that perform all sorts of functions. To execute their functions, proteins often bind to other proteins,” said Eismann. “If you know that a pair of proteins is implicated in a disease and you know how they interact in 3D, you can try to target this interaction very specifically with a drug.”

Eismann and Townshend are co-lead authors of the Science paper with Stanford postdoctoral scholar Andrew Watkins of the Das lab, and also co-lead authors of the Proteins paper with former Stanford PhD student Nathaniel Thomas.

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