Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics.

ABSTRACT: 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 a self-supervised denoising objective, we obtain transferable structural representations that are useful for learning conformational dynamics without further fine-tuning. We show how the learned GNN representations can capture interpretable relationships between structural units (tokens) by combining them with expressive token mixers. Importantly, decoupling training the GNNs from training for down

SUBMITTER: Pengmei Z 

PROVIDER: S-EPMC11722521 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature