Open Force Field
@openforcefield.org
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Reposted by Open Force Field
It's ligand-focussed day at the #CCPBioSim training week, and we've been using @openforcefield.org & @openmm.org to parameterise and run protein-ligand MD, and @mdanalysis.bsky.social & ProLIF for analysis! #compchem
Compared to Sage 2.2.1, Sage 2.3.0-rc2 splits torsions so that a single torsional parameter only covers a central bond with a single multiplicity. It also adds some bond and angle types to improve performance of targeted chemistries, such as small rings.
Sage 2.3.0-rc2 is applicable to drug-like molecules consisting of the elements C, H, O, N, P, S, F, Cl, Br, and I, atomic Xe, and the monoatomic ions Li+, Na+, K+, Rb+, Cs+, F-, Cl-, Br-, and I-.
Sage 2.3.0-rc2 is the second force field candidate in Open Force Field using AshGC v1.0. Both vdW and valence parameters have been re-fit, using AshGC charges, to a dataset of physical properties and QM data.
AshGC supports most small-molecule chemistries with elements C, O, H, N, S, F, Br, Cl, I, P; a list of excluded SMARTS patterns is included in the documentation. docs.openforcefield.org/projects/nag...
AshGC v1.0 is our first neural network charge model. It has been trained to produce charges that are comparable to OpenEye AM1-BCC-ELF10. AshGC 1.0 is identical to the previously released AshGC rc3, except it adds support for single-atom charges.

docs.openforcefield.org/projects/nag...
AM1-BCC GNN v1.0 — OpenFF NAGL Models 2025.9.0.post2+g9309d86.d20250909 documentation
docs.openforcefield.org
Then, in a workflow, you can load Sage 2.3.0 rc2 with

`ff = ForceField("openff-2.3.0-rc2.offxml")`
Or you can update an existing environment:
```
mamba install -c conda-forge "openff-toolkit>=0.17" "openff-interchange>=0.4.6" "openff-nagl>=0.5.3" "openff-nagl-models>=2025.09" "openff-forcefields>=2025.10"
```
The easiest way to try Sage 2.3.0 rc2 out is by making a new environment:
```
mamba create -n openff -c conda-forge "openff-toolkit>=0.17"
mamba activate openff
```
2. Warnings are sometimes emitted about protons/stereo/charges, those are from a performance heuristic and don’t change the chemistry of the inputs (we’re working on silencing these)
Two notes on the example:

1. The first time NAGL is used in a new environment, there may be a few seconds of extra runtime due to pytorch initialization.
...
for idx, mol in enumerate(tqdm(mols, desc="Processing molecules")):
interchange = ff.create_interchange(mol.to_topology())
interchange.minimize()
mol.conformers[0] = interchange.positions
mol.to_file(f"minimized_mol_{idx}.sdf", file_format="sdf")
```
```
from openff.toolkit import Molecule, ForceField
from tqdm import tqdm # for progress bars
mols = Molecule.from_file("inputs.sdf") # loads 50 different molecules
ff = ForceField("openff_unconstrained-2.3.0-rc2.offxml")
...
Because AshGC assigns charges so quickly, use cases like quick conformer minimization are now practical. The following python code will minimize 50 different molecules from an input SDF, saving each minimized structure to an SDF file, in about 1 minute:
Please try it out and let us know if you find any substantial improvements, regressions, or other issues in comparison to Sage 2.2.1. If all goes well, we plan to make the full release in a few weeks! See the thread for an example:
We’re pleased to announce Sage 2.3.0 Release Candidate 2 (rc2)! Sage 2.3.0 will be the first OpenFF force field to use the AshGC neural network charge model, which was trained to AM1BCC ELF10 charges, and allows for rapid charge assignment for molecules with hundreds or thousands of heavy atoms.
OpenFF provides a critical resource for pharmaceutical discovery and design, producing high quality force fields that can be applied at scale without commercial license fees. Learn how your company can partner with us to invest in the future of #openscience app.reclaim.ai/m/james-omsf...
OpenFF / OpenFE informational meeting
Schedule a meeting with leadership of the Open Force Field and Open Free Energy projects to learn more about these projects and how to support them - Powered by Reclaim.ai
app.reclaim.ai
We always love seeing the cool things people do with our force fields!
Reposted by Open Force Field
The advanced bio workshop is in full swing now at the #ccp5 summer school, featuring material from ccpbiosim, @openforcefield.org, mdanalysis & @ppxasjsm.bsky.social. Expertly taught by @matteodegiacomi.bsky.social & @jjguven.bsky.social
We're excited to see this work from @micaelamatta.bsky.social and @hannahturney.bsky.social in publication! They have done some really cool work with our toolkit, and pushed us to to make several performance enhancements for loading and parameterizing polymers.
@hannahturney.bsky.social’s #SwiftPol is now a JOSS paper 🥳
Polymer building made 🪄 easy 🪄 thanks to reaction SMARTS and @rdkit.bsky.social
Just published in JOSS: 'SwiftPol: A Python package for building and parameterizing in silico polymer systems' https://doi.org/10.21105/joss.08053
Our toolkit is designed to meet the needs of force field scientists -- that's who we are! Here's an example of how easy it is to change parameters in a force field in our format: docs.openforcefield.org/en/latest/ex...

#opensource #compchem
Modifying a SMIRNOFF force field — OpenFF Ecosystem documentation
docs.openforcefield.org
It's great to see others using NAGL to train GNN charge models. Stay tuned for our own model, coming soon!
We collect a dataset of quantum mechanical AIM properties computed at a high level of theory (ωB97X-D/def2-tzvpp), in both vacuum and implicit solvent, and use the @openforcefield.org NAGL package to train new GNN charge models to each. github.com/openforcefie...
GitHub - openforcefield/openff-nagl: OpenFF NAGL
OpenFF NAGL. Contribute to openforcefield/openff-nagl development by creating an account on GitHub.
github.com
Reposted by Open Force Field
@jenkescheen.bsky.social showcased his work on @asapdiscovery.bsky.social and more recently with CharmTx, combining ML and more traditional free energy calculations. A lot, if not all, of his work is based on @openfree.energy, #openforcefield, and #alchemsicale. Read his papers for more details!
This tool has solved some of our CI problems.

#opensource #devops