“The network learned to find fundamental concepts that are key to molecular structure formation, but without explicitly being told to,” Townshend added. “The exciting aspect is that the algorithm has clearly recovered things that we knew were important, but it has also recovered characteristics that we didn’t know about before.”

Having shown success with proteins, the researchers turned their attention to RNA molecules. The researchers tested their algorithm in a series of “RNA Puzzles” from a longstanding competition in their field, and in every case, the tool outperformed all the other puzzle participants and did so without being designed specifically for RNA structures.

“We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures,” the authors of the Science article wrote. “The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges.”