Cole Group

We’re pleased to announce the 2026 OpenFF Virtual Workshops! Please join us in March and April for workshops on:

- Simulating Post-Translationally Modified Proteins with the OpenFF Rosemary Alpha
- Fitting a SMIRNOFF Force Field with PyTorch

Details linked:
docs.openforcefield.org/en/latest/wo...

#documentation makes the difference between a piece of code and a tool. @omsf.io is developing deep expertise in documenting #opensource scientific software, and now shares this expertise in a "playbook," including contributions from our own Josh Mitchell.

playbooks.omsf.io/documentation/

We are pleased to invite you to our @iupac.org Global Women’s Breakfast on Tue 10th February from 8:30am in the Boiler House – with free breakfast and refreshments! This year's theme is "Many Voices, One Science… Everyone’s Business".

Please register below:
www.ncl.ac.uk/nes/news/eve...

The first article of volume 7 is out now!

Learn how to simulate molecular dynamics in electronic excited states, beyond the Born-Oppenheimer approximation, with this best practices article by Prlj et al on nonadiabatic dynamics! #compchem

doi.org/10.33011/liv...

Harry has augmented the MACE-OFF training data with soft-core dimer energy curves, and modified the nonlearnable parameters in MACE. This enables calculation of hydration free energies, solvation free energies in octanol, & logP calculations for drug-like molecules, all with sub 1 kcal/mol accuracy.

MACE-OFF is a transferable ML force field for bio-organic / molecular chemistry. MACE-OFF shows high accuracy on gas-phase energetics of small molecules and condensed phase densities/enthalpies, but computing free energies is crucial in drug design applications.

Now out in JACS! 🎉 : "Computing Solvation Free Energies of Small Molecules with Experimental Accuracy"! It's been a pleasure to collaborate on this with Harry Moore (@jhmchem.bsky.social) & Gábor Csányi pubs.acs.org/doi/10.1021/...

Interested in simulating modified nucleic acids?

The latest tutorial article by Galindo-Murillo et al details the steps needed to parameterize and run the simulations in the AMBER ecosystem with modXNA!

#compchem
doi.org/10.33011/liv...

Release Sage 2.3.0 · openforcefield/openff-forcefields This release adds openff-2.3.0.offxml and openff_unconstrained-2.3.0.offxml. Sage 2.3.0 is the first OpenFF force field to use the AshGC neural network charge model to assign charges. Both vdW para...

We’re pleased to announce the full release of the Sage 2.3.0 force field! This is identical to the previous release candidate Sage 2.3.0rc2. Sage 2.3.0 is the first OpenFF force field to use the AshGC neural network charge model. github.com/openforcefie...
#compchem

Developing and benchmarking Sage 2.3.0 with the AshGC neural network charge model We report a new charge model and a new general small molecule force field. Here, we address the development and benchmarking of both the Open Force Field (OpenFF) AshGC charge model, as well as the Sa...

New preprint describing our GNN charge model, AshGC!

Since QM methods of charge assignment scale poorly to larger molecules, and are also conformation dependent, AshGC leads to major performance improvements in this critical step in force field parameterization.

chemrxiv.org/engage/chemr...

The Open Force Field Consortium welcomes two new members to our Governing Board: Daniel Cole (of @colegroupncl.bsky.social ) and Thomas Steinbrecher (of Roche)!

The moment you've been waiting for is here: Registration is NOW OPEN for The 2026 Alchemistry Workshop in Free Energy Methods for Drug Design!

Where: UPF Campus Ciutadella
When: May 4-6, 2026

🔗 Register Today: www.zeffy.com/en-US/ticket...

You can find a thread on the paper here:

bsky.app/profile/cole...

and charge models are openly available here: github.com/cole-group/n...

A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules Common methods for assigning atom-centered partial charges in computational chemistry, such as RESP and AM1-BCC, rely on quantum mechanical or semiempirical calculations of the molecule of interest, which are expensive to compute and dependent on the choice of input molecular conformer(s). Graph neural network (GNN) based continuous atom embeddings have been shown to be a fast and flexible solution for partial charge assignment, but those developed so far for condensed phase modeling have usually been trained to reproduce AM1-BCC charges, which themselves seek to reproduce the HF/6-31G(d) molecular electrostatic potential. Here, we investigate the suitability of various common charge assignment schemes, including ESP and atoms-in-molecule (AIM) based approaches, as training targets for new GNN-based charge models. We show that the strengths of both approaches can be combined by cotraining GNN models to AIM charges and molecular dipoles and electrostatic potentials. We collect a data set of quantum mechanical AIM properties computed at a high level of theory (ωB97X-D/def2-tzvpp), in both vacuum and implicit solvent, and train new GNN charge models to each. Charges can be scaled between the vacuum and solvated charge sets, and combined with Lennard-Jones parameters optimized using the Open Force Field infrastructure, to yield force fields that are suitably polarized for condensed phase modeling. We further demonstrate that the charge models may be applied to explore electrostatics-driven structure–activity relationships in medicinal chemistry. The charge models are freely available at: https://github.com/cole-group/nagl-mbis/.

📢 Now out in #JCTC "A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules". Collaborative effort between @charlie-adams.bsky.social, @chemistryncl.bsky.social, @openforcefield.org & Kuano! #compchem

pubs.acs.org/doi/10.1021/...

Delighted to announce that I've been awarded a #Marsden Fast-Start grant and will be moving back to New Zealand 🇳🇿 to start a position in my hometown at the University of Canterbury!

That's fantastic news, congratulations and very well deserved! 🎉

The latest article in our Lessons Learned category is out now!

"The Journey of Data: Lessons Learned in Modeling Kinase Affinity, Selectivity, and Resistance" by López-Ríos de Castro et al helps guide the development of platforms for structure-enabled ML for drug discovery: doi.org/10.33011/liv...

Feel free to get in touch with any informal questions!

📢 We have a fully funded PhD studentship available for Oct 2026 start on "Training force fields for computer-aided drug design with machine learning", in collaboration with Ioan Magdau and SandboxAQ.

Full details and how to apply: www.ncl.ac.uk/postgraduate...

Closing date: 18 Jan 2026

#compchem

"Enhancing Electrostatic Embedding for ML/MM Free Energy Calculations" is now out in #JCTC: pubs.acs.org/doi/10.1021/...

Great job by João and team! #compchem

If you're at #ukqsar today, be sure to check out posters by @finlayclark.bsky.social, on work with @openforcefield.org, and @asmaferiel.bsky.social & @chikitng.bsky.social on computer-aided drug design methods! #compchem

📢 Looking for a PhD in computational drug discovery? Check out this funded opportunity with @agnesnoy.bsky.social at York, in collaboration with researchers at Newcastle, Oxford & Inspiralis! ⬇️

OpenFold3-preview (OF3p) is out: a sneak peek of our AF3-based structure prediction model. Our aim for OF3 is full AF3-parity for every modality. We now believe we have a clear path towards this goal and are releasing OF3p to enable building in the OF3 ecosystem. More👇

Chapin Cavendar's paper, Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields, is out now in LiveCoMS. Read it for a detailed look at the great work he has been doing toward an OpenFF protein force field, and stay tuned!

livecomsjournal.org/index.php/li...

In the latest @livecomsjournal.bsky.social
perpetual review, Cavender et al overview NMR and crystallographic experimental datasets that can be used to benchmark protein force fields, including best practices for setup and analysis of simulations!:
livecomsjournal.org/index.php/li...

#compchem

Great job by @finlayclark.bsky.social teaching & putting everything together, thanks to Matt Thompson & Jeff Wagner for much of the workshop material & the students for lots of great questions! Full workshop material is here: github.com/openforcefie...

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

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.

Riemannian geometry and molecular similarity II: Kähler quantization | Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences Shape similarity between molecules is a concept used by chemists for virtual screening, with the goal of reducing the cost and duration of drug discovery campaigns. This paper reports an entirely nove...

📣 Pleased to share that the second part of Rachael Pirie's PhD work on 3D shape similarity methods is now available in the Proceedings of the Royal Society A!

@chemistryncl.bsky.social
@mosmedcdt.bsky.social

Code: github.com/RPirie96/KQM...
Article: royalsocietypublishing.org/doi/10.1098/...

Here's why we think this is still important today:
livecomsjournal.org/index.php/li...

But we'd love to continue that debate! What do you like about LiveCoMS, what made you publish with us or read an article? Or what can we do better, what is holding you off submitting your first manuscript?