4 papers
Diffeomorphic Optimization
Ludwig Winkler, Andrew Leaver-Fay, Joseph Kleinhenz +1
Generative models learn data distributions that reside on a low-dimensional manifold within a higher-dimensional ambient space. Optimizing differentiable objectives on this manifol…
Do we need equivariant models for molecule generation?
Ewa M. Nowara, Joshua Rackers, Patricia Suriana +4
Deep generative models are increasingly used for molecular discovery, with most recent approaches relying on equivariant graph neural networks (GNNs) under the assumption that expl…
Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
Frédéric A. Dreyer, Jan Ludwiczak, Karolis Martinkus +8
We introduce Ibex, a pan-immunoglobulin structure prediction model that achieves state-of-the-art accuracy in modeling the variable domains of antibodies, nanobodies, and T-cell re…
Equivariant Neural Tangent Kernels
Philipp Misof, Pan Kessel, Jan E. Gerken
Little is known about the training dynamics of equivariant neural networks, in particular how it compares to data augmented training of their non-equivariant counterparts. Recently…