🔥Today we're excited to announce a major milestone for the machine-learned interatomic potential (MLIP) ecosystem: TorchSim is moving to community ownership and governance through a partnership with Radical AI and the open-source community! MLIPs have become critical

Free Energies of Solvation in Benzene and Hexafluorobenzene: Is Explicit Polarization Needed? | The Journal of Physical Chemistry B

Now published in J Phys Chem B! Check out our work showing that simulations predict the impact of distal mutations on kinase-inhibitor binding, and our experimental NanoBRET dataset of 94 kinase mutations that provide a benchmark for future methods. Link: https://t.co/rGtjbnvRa4

Using ML in binding free energy calculations for drug discovery? Share your work at our #Pacifichem symposium CLH019 "Machine learning for calculation of accurate protein-ligand binding free energies for drug discovery"! Abstracts close April 2. https://t.co/VmxaMNoaC6

A PhD position on the characterization of glycoprotein interactomes is available in my group at the University of Edinburgh's School of Chemistry. This is a competitive application in the scope of the EastBio DTP, for details see

Are you interested in combining #CompChem with #MachineLearning for data-driven catalyst optimization? Check out this exciting #PhD opportunity! In partnership with Antonia Mey (@ppxasjsm). Deadline: 6th January https://t.co/W43SoAdW0s

I have two job adverts open at the minute: - A PhD position on fast MLIPs for biomolecules ( https://t.co/06TAnh6kF2) - A postdoc position on developing a universal molecular mechanics force field ( https://t.co/VxvVfUbjiT) Feel free to get in touch with questions.

Thanks to @julienmich80 and @ColeGroupNCL for supervision. Please try the methods and let us know how they work on your data! Feedback is welcome. (6/6)

We implement all methods in the Python package RED ("conda install conda-forge::red-molsim", https://t.co/tWIfkeHn1o) and provide a complete workflow to reproduce the work ( https://t.co/hRlBunKNmx). All data are available on Zenodo ( https://t.co/mesQFOgjT0). (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 equilibration detection heuristics. Following White ( https://t.co/XwIzKuuYwq), these all work by minimising the marginal standard error, but differ in how they account for autocorrelation. (2/6)

Interested in automated equilibration detection for molecular simulations? Check out our preprint and accompanying Python package, RED: Manuscript: https://t.co/rS9kmWmEEm Python package: https://t.co/tWIfkeHn1o (1/6)

Have you ever wondered how well alchemical free energy methods for zinc-based metallo-proteins with classic forcefields and open-source software tools fare? Then check out our latest preprint by my student @jjguven to find out more:

I for one think that, for ML force fields to be useful for biomolecular simulations, it's okay to be less accurate, but should really be a lot faster, more stable, more interpretable, and more generalizable---a review on the space between MM and ML:

Signed by over 120 experts, our letter highlighting the urgent need for a collaborative effort to establish a #FAIR database for #MolecularDynamics simulation data, is now on Arxiv. 📎 Read it here: https://t.co/VE3qMULPRX 📝Support our statement: https://t.co/7vp58TPy1A #MDDB

🚨JCTC alert 🚨: My work in @shirtsgroupCU with @Michael_Shirts was just published in @jctc_papers @JCIM_JCTC - Replica exchange of expanded ensembles (REXEE): A generalized ensemble approach with enhanced flexibility and parallelizability! https://t.co/r8DgJBctxx #compchem (1/2)

Interested in what OpenFF is doing, but hard to see the big picture from our individual publications? We just published a review/perspective in J Phys Chem B that summarizes/highlights recent work!

A pleasure to work with Ben Ries, @HighSpeedMode, @nithishwer (@BiochemOxford) and @aniket_magarkar to help automate ABFEs into a cluster workflow - Automated Absolute Binding Free Energy Calculation Workflow for Drug Discovery | JCIM