ArticleJournal of chemical information and modeling2023
DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Balancing Data Quantity and Quality: Evaluating Curation Strategies for Bioactivity Prediction in Lead Optimization.Journal of chemical information and modeling · 2026Article
- Permanent electrostatic moments through the lens of atoms: assessing variational Hirshfeld methods.RSC advances · 2026Article
- Unraveling Torsional Preferences: Comparative Analysis of Torsion Motif Torsional-Angle Distributions across Different Environments.Journal of chemical information and modeling · 2025Article
- Atomistic Polymer Modeling: Recent Advances and Challenges in Building and Parametrization Workflows.Macromolecules · 2025Review
- Polarizable Water Model with Ab Initio Neural Network Dynamic Charges and Spontaneous Charge Transfer.Journal of chemical theory and computation · 2025Article
- Validating Small-Molecule Force Fields for Macrocyclic Compounds Using NMR Data in Different Solvents.Journal of chemical information and modeling · 2024Article
- lwreg: A Lightweight System for Chemical Registration and Data Storage.Journal of chemical information and modeling · 2024Article
- Benchmarking Quantum Mechanical Levels of Theory for Valence Parametrization in Force Fields.The journal of physical chemistry. B · 2024Article
- A general graph neural network based implicit solvation model for organic molecules in water.Chemical science · 2024Article
- Parameterization of General Organic Polymers within the Open Force Field Framework.Journal of chemical information and modeling · 2024Article
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Authors and funding
7 authors.
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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.
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