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ashgc-v1.0-data

Name: ashgc-v1.0-data
Creator: Open Force Field
License: https://choosealicense.com/licenses/cc-by-4.0/

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Dataset Card for OpenFF AshGC v1.0 data

This dataset includes the training, validation, and testing data for the AshGC v1.0 charge model, a graph convolutional neural network designed to assign partial atomic charges of semi-empirical quality to molecules for usage with Open Force Field (OpenFF) force fields.

AshGC is designed to efficiently produce conformer-independent charges of semi-empirical quality at linear computational cost for molecules ranging from small molecules to macromolecules.

Dataset Details

Dataset Description

The OpenFF AshGC v1.0 dataset contains molecular graphs (as mapped SMILES) and corresponding AM1-BCC ELF10 (Electrostatically Least-interacting Functional groups, 10-conformer) partial atomic charges for training and evaluating the AshGC neural network charge model.

For training and validation purposes, datasets also include conformers and computed geometries.

The dataset comprises 271,204 molecules in the initial training set, 39,909 molecules in the fine-tuning set (small molecules < 200 Da), 61,593 molecules in the validation set, and 45,596 molecules in the test set. Molecules span diverse chemical space including drug-like molecules, peptides up to 7 residues (heptamers), and molecules with post-translational modifications.

The set of elements covered includes H, C, N, O, F, P, S, Cl, Br, I. Molecules range from 1 to 82 heavy atoms and -6 to 8 molecular charge.

Curated by: Open Force Field Initiative, Open Molecular Software Foundation
Funded by: Open Force Field Initiative
Shared by: Open Force Field Initiative
Language(s): Not applicable (molecular data)
License: CC BY 4.0

Dataset Sources [optional]

Repository: https://github.com/openforcefield/ashgc-v1.0-fit
Paper: https://chemrxiv.org/doi/full/10.26434/chemrxiv-2025-597h9

Uses

Direct Use

This dataset was used for:

Training graph neural networks to predict partial atomic charges for molecular dynamics simulations
Benchmarking charge assignment methods against AM1-BCC ELF10 reference charges
Benchmarking differences between AM1-BCC backends between AmberTools and OpenEye (the reference)

Out-of-Scope Use

This dataset should not be used for:

Molecules containing elements outside the supported set: H, C, N, O, F, P, S, Cl, Br, I
Systems requiring highly accurate quantum mechanical charge distributions (the AM1-BCC charges are semi-empirical quality)
Combined with datasets of primarily AmberTools charges, as there are systematic differences between OpenEye and AmberTools. (Note, a subset of the data here does contain AmberTools charges and may be used)
Combined with datasets of single-conformer AM1-BCC charges, as the charges here are averaged over multiple conformers

Dataset Structure

The schema is:

mapped_smiles: mapped SMILES
conformers: all conformer geometries in Angstrom, flattened into a list
n_conformers: the number of conformers in conformers
esp_lengths: a list of the number of ESP grid points per conformer
am1bcc_charges: AM1-BCC ELF10 charges
am1bcc_esps: computed ESPs using AM1-BCC ELF10 charges
am1bcc_dipoles: computed dipoles using AM1-BCC dipoles
esp_grid_inverse_distances: the inverse distances from each atom to each ESP grid point, for ease of ESP computation

Some subsets contain AmberTools charges as well. In these cases, the AmberTools charges are marked either by folder name (starting with ambertools_) or by column name (ending with _ambertools). Otherwise, charges should be assumed to originate from OpenEye's oequacpac.

Dataset Creation

Curation Rationale

The AshGC dataset was created to train and benchmark the AshGC v1.0 charge model. Please see the preprint for more information.

Source Data

A brief summary is provided for more, but please see the paper for more information.

Data Collection and Processing

Initial molecular graphs were collected from:

ChEMBL eps 78 and ZINC eps 78 (Bleizziffer et al. 2018)
Enamine Discovery Diversity Sets (DDS-10 and DDS-50, accessed April 2023)
NCI Open Database (Release 4, 2012)
PDB Ligand Expo (accessed April 2023)
ChEMBL v33 (accessed October 2023)
OpenFF Industry Benchmark Season 1 v1.1
SPICE dataset v1.1.0

Data processing pipeline:

Molecular filtering:
- Most datasets filtered to 200-400 Da molecular weight
- Elements limited to H, C, N, O, F, P, S, Cl, Br, I
- Up to 10 rotatable bonds
- ChEMBL eps 78 and ZINC eps 78: 250-350 Da, up to 7 rotatable bonds
- Small-chembl: 1-199 Da (no rotatable bond filter)
Protomer enumeration:
- Up to 2 protomers per molecule using OpenEye oequacpac (OpenEye version 2022.2.1)
Conformer generation:
- 1000 conformers per molecule using OpenEye OMEGA
- RMSD cutoff: 0.05 Å
- Lowest 2% by electrostatic energy selected
- Up to 10 diverse conformers chosen via ELF10 method
Charge generation:
- Single-conformer AM1-BCC charges computed for each conformer
- Charges averaged across all conformers to produce final ELF10 charges
- Generated using both OpenEye Toolkit v2022.2.1 and AmberTools v22.0 (via OpenFF Toolkit v0.11.1)
Diversity selection:
- Molecules labeled using hashed atom pair fingerprints (RDKit v2022.03.5, maxLength=2, 2048 bits)
- Molecules with rare atom environments selected
- Top 4 molecules per underrepresented environment added
Train/validation/test splits:
- Morgan fingerprints (radius 3, 2048 bits) computed
- Training set selected by MaxMin algorithm (Tanimoto distance)
- Test set filtered to < 0.7 Tanimoto similarity with training/validation

Peptide datasets:

Two specialized peptide datasets were generated to extend coverage to larger biomolecules using RDKit v2022.03.5:

Tetrapeptides: 1-4 amino acid chains from 20 natural amino acids (combinations with replacement)
PTMs (heptamers): "G-V-triplet-V-G" structure with post-translational modifications (lipidation, phosphorylation, glycosylation, nitrosylation)