Our most recent publication with our collaborators at Novartis Biomedical Research uses the idea of Cooperative Free Energy in order to understand the thermodynamics of cooperativity in ternary complexes. pubs.acs.org/doi/10.1021/...

Our newest publication explores using QM/MM together with RE-EDS to do efficient free-energy calculations, enabling increased accuracy and allowing highly polarized systems to be studied. #chemsky #compchem doi.org/10.1021/acs....

Our new paper in JACS presents the application of a new neural network potential, based on our anisotropic message passing approach, to do QM/MM MD simulations. We achieve chemical accuracy on a few quite different types of chemistry. doi.org/10.1021/jacs... #chemsky #compchem #opensource

Our newest preprint describes lwreg, a lightweight system for chemical registration. lwreg has a Python API and makes it easy to store the compound structures you use in your work and the experimental data you generate about them. chemrxiv.org/engage/chemr...

Our paper introducing an implicit solvation model for organic molecule in water based on a graph neural network has just appeared: pubs.rsc.org/en/content/a... #chemsky

The most recent paper from our ongoing collaboration with the Zenobi group looks at the impact of desolvation on the stability of beta-hairpin structures. #chemsky pubs.acs.org/doi/10.1021/...

Our newest preprint, introducing a new type of implicit solvation model based on a graph neural network, is now up: chemrxiv.org/engage/chemr...

In our newest paper we look at using five different computational methods applied to the results of MD simulations and NMR relaxation experiments in order to better understand protein motions. doi.org/10.1063/5.01...

Our most recent paper just appeared in JCIM. The title of this one pretty much tells the story: when you assemble a data set by combining data from different literature assays, there is a very good chance that the resulting data contains a lot of noise. pubs.acs.org/doi/10.1021/...

We have a new preprint out which looks at the amount of noise introduced into a data set when we combine data from different ChEMBL assays. doi.org/10.26434/che...

Our paper introducing SIMPD is now out. SIMPD is an algorithm for creating training/test sets for molecular #machinelearning based on an analysis of a large number of real-world medchem projects. link.springer.com/article/10.1... #opensource code and data are in github. github.com/rinikerlab/m...

Our most recent preprint describes research done together with the Ferrage group at the Sorbonne in Paris to apply #MolecularDynamics and #NMR to understand protein motions in solution. chemrxiv.org/engage/chemr...

Our most recent publication describes a hybrid classical/machine-learning forcefield we've developed for condensed-phase systems. As usual, it's #opensource, #opendata, and #openaccess pubs.rsc.org/en/content/a...

Our paper introducing DASH, an efficient approach for assigning partial charges to atoms in molecules is now out. The method uses a hierarchy created from attention values from a GNN trained on QM data. It's #opensource, #opendata, and #openaccess pubs.acs.org/doi/10.1021/...