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  3. Developing And Benchmarking Sage 2.3.0 With The Ashgc Neural Network Charge Model.
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  3. Developing And Benchmarking Sage 2.3.0 With The Ashgc Neural Network Charge Model.

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Developing and Benchmarking Sage 2.3.0 with the AshGC Neural Network Charge Model.

Lily Wang1, Irfan Alibay2, Pavan Kumar Behara3

  • 1Open Force Field, Open Molecular Software Foundation, Davis, California 95616, United States.

Journal of Chemical Theory and Computation
|April 21, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed Open Force Field (OpenFF) AshGC, a graph convolutional neural network for efficient partial atomic charge assignment, and the Sage 2.3.0 force field. These tools offer semiempirical quality charges at linear cost for molecules of all sizes.

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Area of Science:

  • Computational Chemistry
  • Molecular Modeling
  • Drug Discovery

Background:

  • Assigning partial atomic charges is crucial for molecular simulations but computationally expensive.
  • Existing methods often rely on quantum mechanics, which scales poorly and is sensitive to molecular geometry.

Purpose of the Study:

  • Introduce Open Force Field (OpenFF) AshGC, a novel graph convolutional neural network charge model.
  • Develop the Sage 2.3.0 small molecule force field, consistent with AshGC charges.
  • Provide efficient, conformer-independent charges for molecules of all sizes.

Main Methods:

  • Developed AshGC, a graph convolutional neural network for charge assignment.
  • Parametrized the Sage 2.3.0 force field with Lennard-Jones and valence parameters consistent with AshGC.
  • Benchmarked performance across gas-phase geometry optimization and protein-ligand binding free energies.

    • Main Results:

    • AshGC produces conformer-independent charges of semiempirical quality at linear cost.
    • Sage 2.3.0 shows comparable performance to previous releases, with improved condensed-phase properties.
    • AshGC charges align with established methods like AM1-BCC, with minor deviations in specific chemical areas.

    Conclusions:

    • AshGC and Sage 2.3.0 offer an efficient and accurate approach to charge assignment and molecular simulation.
    • The new force field is compatible with existing Amber protein force fields.
    • All data and code are publicly available for reproducibility.