Dataset Information

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.

SUBMITTER: Lehner MT

PROVIDER: S-EPMC10565818 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment.

Lehner Marc T MT Katzberger Paul P Maeder Niels N Schiebroek Carl C G CCG Teetz Jakob J Landrum Gregory A GA Riniker Sereina S

Journal of chemical information and modeling 20230922 19

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, ...[more]

PMID: 37738206

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Dataset Information

DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment.

Publications

DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment.

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