Before revision:  Despite using only spatial coordinates and identities of atoms as inputs , SASNet outperforms state-of-the-art methods that rely on hand-engineered , high-level features. These results are particularly striking because we train the method entirely on a significantly biased dataset that does not account for the fact that proteins deform when binding to one another. Demonstrating the first successful application of transfer learning to atomic-level data, our network maintains high performance, without retraining, when tested on real cases in which proteins do deform.
After revision:  Despite using only spatial coordinates and identities of atoms as inputs , SASNet outperforms state-of-the-art methods that rely on hand-engineered , even when trained on only 3\% of our new dataset. Code and data available at URL