Our new FEP sampling engine GrandFEP is now on GitHub and Chemrxvi. We implemented GCMC, Water-Swap MC, REST2, and terminal-flip MC in OpenMM.

Github github.com/deGrootLab/G...
Chemrxiv doi.org/10.26434/che...

RSC CICAG Chemical Structure Representations Meeting 2026
Burlington House, London, UK
Wednesday 8th April
registrations.hg3conferences.co.uk/hg3/frontend...
Fabulous line up of speakers.

Training a force field for proteins and small molecules from scratch Force fields for molecular dynamics are usually developed manually, limiting their transferability and making systematic exploration of functional forms challenging. We developed a graph neural networ...

Check out our pre-print, where we train a protein and small molecule force field from scratch with a graph neural network.

We show comparable performance to existing, manually-tuned force fields on a range of tasks including binding free energy prediction. (1/4)

arxiv.org/abs/2603.16770

The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine-learning-based methods, necessitates a new collaborative architecture to replace the isolate...

Modern molecular modeling needs a new mode of software development. Consortia like Open Free Energy build shared tools and release code under open licenses. @omsf.io aligns incentives across stakeholders, enabling an ecosystems that elevates the entire community. pubs.acs.org/doi/10.1021/...

The body ordered expansion, equations

Many #machinelearning potentials are built (or understood) in terms of "atomic cluster expansions" that link directly to a body-ordered energy decomposition that can be computed explicitly with a sequence of electronic structure calculations. But what kind of expansion do they learn in practice? A🧵

Robust and Automated Force Field Parameterization Using Validation Sets and Active Learning Molecular mechanics force fields enable atomistic simulations of complex systems that are too large for a quantum mechanical treatment. Simulation accuracy depends on the parameters employed in the fo...

"In contrast to previous attempts at iterative optimization, we employ a validation set to determine convergence. Using a validation set circumvents problems with parameter convergence and flags when overfitting occurs"

pubs.acs.org/doi/full/10....

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/...

📢 Can AI-Predicted Complexes Teach Machine Learning to Compute Drug Binding Affinity?

In our recent JCIM work, we tested whether co-folding models can be used for data augmentation for training ML-based scoring functions (SFs).

We asked 3 simple but critical questions. 👇
(1/6)

Ever wanted to run MD simulations of entire proteins in water with DFT accuracy?

Meet AMPv3-BMS25, the latest iteration of our AMP multiscale neural network potential by
@rinikerlab.bsky.social

Read more in the preprints:
doi.org/10.26434/che...
doi.org/10.26434/che...

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

Large-scale collaborative assessment of binding free energy calculations for drug discovery using OpenFE Accurately measuring compound binding affinities is key to driving the pharmaceutical development process. Rigorous physics-based in silico approaches, particularly alchemical free energy methods, hav...

OpenFE is ready for production! chemrxiv.org/engage/chemr...

In collaboration with our industry partners, we ran benchmarking simulations of our hybrid-topology RBFE protocol on a large collection of both public and private protein-ligand binding datasets.

#compchem

New preprint!
How many crystal structures do you need to trust your docking results? It turns out, if you know how your main scaffold binds in the pocket, not that many! Check out this work from recently-defended lab member @keysandcompounds.bsky.social

On BioRxiv:
www.biorxiv.org/content/10.1...

Can AI-Predicted Complexes Teach Machine Learning to Compute Drug Binding Affinity? We evaluate the feasibility of using co-folding models for synthetic data augmentation in training machine learning-based scoring functions (MLSFs) for binding affinity prediction. Our results show th...

Congrats to Wei-Tse Hsu on our latest paper with Aniket Magarkar @boehringerglobal.bsky.social where we look at how well co-folding models might be used in the context of data-augmentation for affinity prediction models.

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

📢 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! ⬇️

📢 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

Excited to be a part of this review paper by Chapin Cavender et al. Now out in LiveCoMS! doi.org/10.33011/liv...

2.5.0 · alchemistry alchemlyb · Discussion #445 New minor release of alchemlyb with fixes and enhancements. Supports Python 3.11 - 3.14. See CHANGES for details. What's Changed update action/checkout by @orbeckst in #417 Parallel read and prepro...

If you're doing #alchemistry (alchemical free energy calculations) then here's release 2.5.0 of alchemlyb for you. github.com/alchemistry/...

(There's also the alchemlyb @joss-openjournals.bsky.social paper doi.org/10.21105/jos... if you want to read & cite.)

Preprint release 😀 of "Speak to a Protein," an AI co-scientist that facilitates data gathering and analysis in an interactive collaborative session. It is quite amazing to use. Preprint: arxiv.org/abs/2510.17826

Crystallography for the "dark proteome"? From @nudrugdiscovery.bsky.social and @chemistryncl.bsky.social : #FragLites map protein–protein interaction sites including regions with no previously known function. 🧵1/3 #DrugDiscovery #ChemBio
📖 www.sciencedirect.com/science/arti...

Long-Range Interactions in High-Dimensional Neural Network Potentials: A Benchmark Study for Small Organic Molecules Many machine learning potentials (MLPs) rely on representations of the total energy in terms of the positions of the atoms in their local environment, using either a cutoff radius or a limited number ...

Happy to share Phuc's new preprint in collaboration with Jorg Behler on the integration of our MLXDM dispersion term into 4th generation charge equilibration NNPs chemrxiv.org/engage/chemr...

Can AI-predicted complexes teach machine learning to compute drug binding affinity? We evaluate the feasibility of using co-folding models for synthetic data augmentation in training machine learning-based scoring functions (MLSFs) for binding affinity prediction. Our results show th...

Latest pp with Aniket Magarkar @boehringerglobal.bsky.social - We evaluated the potential of co-folding models to generate synthetic protein–ligand complexes for training machine learning-based scoring functions. arxiv.org/abs/2507.07882. Btw - Wei-Tse Hsu at this GRC tinyurl.com/3knvp3wn

Scalable emulation of protein equilibrium ensembles with generative deep learning Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. We introduce BioEmu, a deep learning system that...

BioEmu is now on @science.org ! The revised version includes an upgraded model and makes a lot of MD simulation data internally generated at MSR available to the public. This took a lot of firepower from us in the last two years.
www.science.org/doi/10.1126/...

Computing hydration free energies of small molecules with first principles accuracy Free energies play a central role in characterising the behaviour of chemical systems and are among the most important quantities that can be calculated by molecular dynamics simulations. The free ene...

Big update to @jhmchem.bsky.social's preprint on "Computing solvation free energies of small molecules with first principles accuracy" now available on arXiv: arxiv.org/abs/2405.181... #compchem 🧵

Simple Method to Optimize the Spacing and Number of Alchemical Intermediates in Expanded Ensemble Free Energy Calculations Alchemical free energy calculations are essential to modern structure-based drug design. Such calculations are usually performed at a series of discrete intermediates along a nonphysical thermodynamic...

Excited to share work now out in #JCIM @acs.org !
doi.org/10.1021/acs....

Alchemical free energy calculations are indispensable in computational drug design. We present an improved approach for optimizing the schedule of alchemical intermediates by minimizing thermodynamic length.

1/7

"Enhancing Electrostatic Embedding for ML/MM Free Energy Calculations", by Joao Morado et al, is now available on ChemRxiv! doi.org/10.26434/che... #compchem

Are you an EPSRC funded PGR? Ready to submit your thesis this year, or just passed your viva and looking for a postdoc position? Get in touch if interested in apply for a pathway fellowship in the areas of molecular modelling or computer-aided drug design ⬇️

jobs.ncl.ac.uk/job/Newcastl...

📢 New preprint: "A graph neural network charge model targeting accurate electrostatic properties of organic molecules" by @charlie-adams.bsky.social et al out now on @chemrxiv.bsky.social #compchem

doi.org/10.26434/che...

All Roads Lead to Carbinolamine: QM/MM Study of Enzymatic C-N Bond Cleavage in Anaerobic Glycyl Radical Enzyme Choline Trimethylamine-Lyase (CutC) The anaerobic glycyl radical enzyme choline trimethylamine-lyase (CutC) is produced by multiple bacterial species in the human gut microbiome and catalyzes the conversion of choline to trimethylamine ...

Go check out our preprint on simulations of CutC on ChemRxiv!

Marko Hanževački has done great work on this and it’s always a pleasure to work with him and @adrianmulholla1.bsky.social 🚀

chemrxiv.org/engage/chemr...