Finlay Clark

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...

Asymmetric Nature of MscL Opening Revealed by Molecular Dynamics Simulations The bacterial mechanosensitive channel, MscL, opens in response to elevated membrane tension during osmotic shock. Some mutations, like L17A and V21A, can reduce the activation tension threshold, thus...

MscL channel opens up asymmetrically! Check out our latest story, spearheaded by my brilliant postdoc Olga

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

MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required for first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short-range transferable force fields for organic molecules created using state-of-the-art machine learning technology and first-principles reference data computed with a high level of quantum mechanical theory. MACE-OFF demonstrates the remarkable capabilities of short-range models by accurately predicting a wide variety of gas- and condensed-phase properties of molecular systems. It produces accurate, easy-to-converge dihedral torsion scans of unseen molecules as well as reliable descriptions of molecular crystals and liquids, including quantum nuclear effects. We further demonstrate the capabilities of MACE-OFF by determining free energy surfaces in explicit solvent as well as the folding dynamics of peptides and nanosecond simulations of a fully solvated protein. These developments enable first-principles simulations of molecular systems for the broader chemistry community at high accuracy and relatively low computational cost.

Promising paper. And whoever thought up the title deserves an award. pubs.acs.org/doi/10.1021/...

Electrophysiology at atomic resolution: scientists simulate ion channel currents with unprecedented accuracy

www.qmul.ac.uk/media/news/2...

The CCPBioSim Annual Conference - Frontiers in Biomolecular Simulations will be taking place in Southampton, 14-16 July 2025. Registration is now open! Details and the registration link can be found at www.ccpbiosim.ac.uk/soton2025 #compchem

Passionate about force fields? Got great ideas for the future of force field design?

We are looking to support applicants to the #MSCA Postdoctoral Fellowships scheme in collaboration with @openforcefield.org!

The call opens soon, get in touch if interested ⬇️ tinyurl.com/yxbpj4y4

The OpenFF toolkit contains some facilities for visualizing molecules in a Jupyter notebook, which can be a convenient way to inspect the system you're setting up.

docs.openforcefield.org/en/latest/ex...

#compchem

Share your own examples of visualizing molecules!

Header of the webpage showing the title ("Atomistic Water model for MD") and the authors (Philip Loche, Marcel Langer, Michele Ceriotti)

Happy to share a new #cookbook recipe that shocases several new software developments in the lab, using the good ole' QTIP4P/f water model as an example. atomistic-cookbook.org/examples/wat.... TL;DR - you can now build torch-based interatomic potentials, export them and use them wherever you like!

Excited to share what I’ve been working on with @tkaraletsos.bsky.social to transform the field of drug discovery with atomistic foundation simulation models!

I could not be more thrilled to be working with this dream team.

achira.ai

CCPBioSim Annual Conference 2025 More detail will be added here very soon!

📢 SAVE THE DATE: This year's annual CCPBioSim conference will be in sunny Southampton on the 14th-16th July 2025.

Stay tuned on here and at ccpbiosim.ac.uk/soton2025 for more information and announcements coming soon!

#compchem #chemsky

SAVE THE DATE: We’re hosting this years annual CCPBioSim conference in sunny Southampton on the 14th-16th July 2025. Stay tuned on socials and at ccpbiosim.ac.uk/soton2025 for more information and announcements coming soon!

Thanks to Julien Michel and @colegroupncl.bsky.social
for supervision. Please try the methods and let us know how they work on your data! Feedback is welcome.

(6/6)

GitHub - fjclark/red: Robust Equilibration Detection Robust Equilibration Detection. Contribute to fjclark/red development by creating an account on GitHub.

We implement all methods in the Python package RED ("conda install conda-forge::red-molsim", github.com/fjclark/red) and provide a complete workflow to reproduce the work (github.com/michellab/Ro...). All data are available on Zenodo (zenodo.org/records/1390...).

(5/6)

We observe a general trade-off: methods which more thoroughly account for autocorrelation often discard too much data, while methods which less thoroughly account for autocorrelation often discard too little data. We recommend a method which balances these extremes.

(4/6)

To quantitatively assess the heuristics, we create sets of synthetic data modelled on long alchemical absolute binding free energy calculations. Since we know the true unbiased mean of our synthetic data, we can calculate the errors each of the heuristics introduces.

(3/6)

We test a range of truncation point selection heuristics. Following White (doi.org/10.1177/0037...), these all work by minimising the marginal standard error, but differ in how they account for autocorrelation.

(2/6)

Robust Automated Truncation Point Selection for Molecular Simulations Quantities calculated from molecular simulations are often subject to an initial bias due to unrepresentative starting configurations. Initial data are usually discarded to reduce bias. Chodera’s method for automated truncation point selection [J. Chem. Theory Comput. 2016, 12, 4, 1799–1805] is popular but has not been thoroughly assessed. We reformulate White’s marginal standard error rule to provide a spectrum of truncation point selection heuristics that differ in their treatment of autocorrelation. These include a method effectively equivalent to Chodera’s. We test these methods on ensembles of synthetic time series modeled on free energy change estimates from long absolute binding free energy calculations. Methods that more thoroughly account for autocorrelation often show late and variable truncation times, while methods that less thoroughly account for autocorrelation often show early truncation, relative to the optimal truncation point. This increases variance and bias, respectively. We recommend a method that achieves robust performance across our test sets by balancing these two extremes. None of the methods reliably detected insufficient sampling. All heuristics tested are implemented in the open-source Python package RED (github.com/fjclark/red).

Interested in automated truncation point selection ("equilibration detection") for molecular simulations? Check out our recent paper and accompanying Python package, RED:

Paper: pubs.acs.org/doi/10.1021/...
Python package: github.com/fjclark/red

(1/6)