#compchem New paper in JCTC JCIM & JCTC Journals : "Velocity Jumps for Molecular Dynamics" pubs.acs.org/doi/10.1021/ac… We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a

#compchem Happy to see this one out in Nature Communications: "Histidine 73 methylation coordinates beta-actin plasticity in response to key environmental factors". nature.com/articles/s4146… We used large scale molecular dynamics simulations coupling adaptive sampling and the AMOEBA

A Foundation Model for Accurate Atomistic Simulations in Drug Design 1. This paper introduces FeNNix-Bio1, a foundation machine-learning model for atomistic molecular dynamics simulations, aimed at revolutionizing drug design by providing accurate simulations that include

#compchem Just published in JCIM JCIM & JCTC Journals: "AMOEBA Polarizable Molecular Dynamics Simulations of Guanine Quadruplexes: from the c-Kit Proto-oncogene to HIV-1." Paper: pubs.acs.org/doi/10.1021/ac… Updated preprint: biorxiv.org/content/10.110… Very nice work by Dina Ahdab (El), PhD. Another

#compchem New paper in J. Phys. Chem. Lett The Journal of Physical Chemistry : "Lambda-ABF-OPES: Faster Convergence with High Accuracy in Alchemical Free Energy Calculations". Paper: pubs.acs.org/doi/10.1021/ac… preprint: arxiv.org/abs/2502.17233 To compute absolute binding free energies, this approach

FeNNix-Bio1: an Highly scalable foundation #machinelearning model for biosimulations in Tinker-HP/Deep-HP #compchem #HPC

#compchem What could we do if we had a fast and accurate foundation #MachineLearning learning model for condensed phase molecular dynamics simulation of biological systems? 👉Check the ChemRxiv preprint: "A Foundation Model for Accurate Atomistic Simulations in Drug Design":

#compchem FeNNix-Bio1's full-range of capabilities is demonstrated by modelling: water properties, ions in solution, small molecules hydration free energies, complex folding free-energy landscapes, large-scale protein dynamics, protein-ligand binding and chemical reactions. We

AI + physics turns dynamics into a data engine. FeNNix-Bio1: a foundation model that drives QM-level, reactive MD on a single GPU, hydration, folding, binding ΔG within ≈1 kcal/mol, yet pushes 7M-atom boxes at force-field speed

This is exciting stuff. A Foundation Model for Accurate Atomistic Simulations in Drug Design chemrxiv.org/engage/chemrxi…

#compchem The FeNNix-Bio1 foundation model's inference is fast and leverages multi-GPUs computing systems (here NVIDIA 's H100 nodes). It is also designed so learning a new model remains economical (1 card or node depending on the model size) and can be performed in 48 hours.

#compchem Second preprint linked to the FeNNix-Bio1 #machinelearning foundation model. FeNNix-Bio1's inference is pretty fast already with a few GPUs but, "what if", we were able to push it at the #Exascale? Let's have a glimpse into the future (1/3): "Pushing the Accuracy Limit

(3/3) To bridge the gap between accurate quantum chemistry and condensed-phase Molecular Dynamics, we leverage transfer learning to improve the DFT-based FeNNix-Bio1 foundation model using the DMC/sCI energies and forces. The resulting approach is coupled to path integrals

#compchem Adaptive sampling (reactive) simulations of the 1M-atom STMV using the FeNNix-Bio1 foundation neural network model coupled to quantum - path integrals- MD. Using a "beyond DFT" version of the model, the PH transition (7 to 5) is studied reaching unprecedented accurary.

The 2025 Tinker developers meeting group photo. Thanks everyone! #compchem

If you want to know more about #FeNNix-Bio1, the first foundation model able to perform accurate - long timescale- condensed phase molecular simulations of biological systems at quantum accuracy, join me in incoming live presentations: • NVIDIA #GTC25 (Paris)

#compchem #compbio Happy to be part of this new paper just published in Communications Biology : "Targeting RNA with Small Molecules using State-of-the-Art Methods Provides Highly Predictive Affinities of Riboswitch Inhibitors." Here, we tackle these difficulties by developing a tailored

New paper introducing faster #machinelearning potentials molecular dynamics simulations. #compchem