geom2vec: pretrained GNNs as geometric featurizers for conformational
dynamics
geom2vec: pretrained GNNs as geometric featurizers for conformational
dynamics
Identifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive hand-tuning and system-specific knowledge. Here, we introduce geom2vec, in which pretrained graph neural networks (GNNs) are used as universal geometric featurizers. By pretraining equivariant GNNs on a large dataset of molecular conformations with a …