going beyond the Euclidean plane would require them to reimagine one of the basic computational procedures that made neural networks so effective at 2D image recognition in the first place. This procedure, called “convolution,” lets a layer of the neural network perform a mathematical operation on small patches of the input data and then pass the results to the next layer in the network. Gauge CNNs can detect patterns not only in 2D arrays of pixels, but also on spheres and asymmetrically curved objects. “This framework is a fairly definitive answer to this problem of deep learning on curved surfaces”
this has been applied to proteins:
Correia’s system, called MaSIF (short for molecular surface interaction fingerprinting), avoids the inherent complexity of a protein’s 3D shape by ignoring the molecules’ internal structure. Instead, the system scans the protein’s 2D surface for what the researchers call interaction fingerprints: features learned by a neural network that indicate that another protein could bind there. “The idea is that when any 2 molecules come together, what they’re essentially presenting to 1 another is that surface. So that’s all you need,. It’s very, very innovative.”
