DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment
Abstract
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic attention-based substructure hierarchy (DASH) approach provides fast assignment of partial charges with the same accuracy as the GNN itself, is software-independent, and can easily be integrated in existing parametrization pipelines, as shown for the Open force field (OpenFF). The implementation of the DASH workflow, the final DASH tree, and the training set are available as open source/open data from public repositories. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000640324Publication status
publishedExternal links
Journal / series
Journal of Chemical Information and ModelingVolume
Pages / Article No.
Publisher
American Chemical SocietyOrganisational unit
09458 - Riniker, Sereina Z. / Riniker, Sereina Z.
Funding
212732 - Combining Molecular Dynamics and Machine Learning for Free Energy Calculation with Quantum-Mechanical Accuracy (SNF)
ETH-50 21-1 - Development of an Improved Implicit Solvation Approach Through Machine Learning from Molecular Dynamics Simulations (ETHZ)
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