Determining the 3D shapes of biological molecules is one of the hardest challenges 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.
Through novel machine-learning techniques, Stanford PhD students Stephan Eismann and Raphael Townshend, under the guidance of associate professor of computer scientist Ron Dror, have developed an approach that overcomes this problem by predicting accurate structures computationally.
The team said, most notably, their approach succeeds even when learning from only a few known structures, making it applicable to the molecules whose structures are most difficult to determine experimentally. “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...