Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics
Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics
Identifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive manual 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 …