Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks

• URL: http://arxiv.org/abs/2106.13277v1
• Date: Thu, 24 Jun 2021 19:01:05 GMT
• Title: Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks
• Authors: Michael Hunter Ashby and Jenna A. Bilbrey
• Abstract summary: We apply a temporal edge prediction model for weighted dynamic graphs to predict time-dependent changes in molecular structure. We ingest a sequence of complete molecular graphs into a dynamic graph neural network (GNN) to predict the graph at the next time step. Our dynamic GNN predicts atom-to-atom distances with a mean absolute error of 0.017 rA, which is considered chemically accurate'' for molecular simulations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We apply a temporal edge prediction model for weighted dynamic graphs to predict time-dependent changes in molecular structure. Each molecule is represented as a complete graph in which each atom is a vertex and all vertex pairs are connected by an edge weighted by the Euclidean distance between atom pairs. We ingest a sequence of complete molecular graphs into a dynamic graph neural network (GNN) to predict the graph at the next time step. Our dynamic GNN predicts atom-to-atom distances with a mean absolute error of 0.017 \r{A}, which is considered ``chemically accurate'' for molecular simulations. We also explored the transferability of a trained network to new molecular systems and found that finetuning with less than 10% of the total trajectory provides a mean absolute error of the same order of magnitude as that when training from scratch on the full molecular trajectory.

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