NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:9078
Title:End-to-End Learning on 3D Protein Structure for Interface Prediction

The main contributions of this paper, developing the SASNet model, compiling a large DIPS dataset, and empirical results, are of high significance. The paper is very clearly written and well organized, and the authors’ claims are well supported by experimental results. Although some reviewers thought that there was room for improvement in the way the idea is executed, they all agreed that the paper deserves acceptance to NeurIPS.