Applying for an ERC grant in the 2027 competitions: what you need to know The ERC plans to launch the grant competitions under its 2027 Work Programme between July 2026 and June 2027, with the calls for proposals introducing several changes to the eligibility rules for appl...

I don’t know if you saw the MASSIVE news announced by @erc.europa.eu today: from now on, if you get a B at step 1 you are eligible to apply at N+3(!!!) years. Say you got a B in STG2026 step 1, you thought you could apply in STG2028, but no: only in STG2029! erc.europa.eu/news-events/...

New group preprint: "Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces". Great work by N. Gouraud.

arxiv.org/abs/2602.14975

Quantum Computing for Quantum Chemistry Register for Telluride Science!

Happy to participate to the "𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐂𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲, 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐃𝐲𝐧𝐚𝐦𝐢𝐜𝐬, 𝐚𝐧𝐝 𝐁𝐞𝐲𝐨𝐧𝐝" workshop, a great meeting organized by A. Izmaylov (U. Toronto) & Y. Zhang (Los Alamos ) at TSRC Telluride, CO.
#quantumcomputing #compchem quantum-computing-for-quantum-chemistry.raiselysite.com

Will Quantum Computing actually transform #drug discovery? In the age of AI, why are we still betting on Quantum & GPU-accelerated HPC? We’ve just released our whitepaper detailing how the synergy of #QuantumComputing #MachineLearning & #HPC enables quantum-accurate simulations. #compbio #compchem

#compchem #machinelearning #quantumcomputing #compbio New preprint: "The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery".
@qubit-pharma.bsky.social
arxiv.org/abs/2603.17790

Logarithmic-depth quantum state preparation of polynomials Quantum state preparation is a central primitive in many quantum algorithms, yet it is generally resource intensive, with efficient constructions known only for structured families of states. This wor...

New #quantumcomputing group preprint in collaboration with @qubit-pharma.bsky.social and CERFACS:
"Logarithmic-depth quantum state preparation of polynomials"
arxiv.org/abs/2603.16527

Our recent #quantumcomputing work "Experimental Realization of the Markov Chain Monte Carlo Algorithm on a Quantum Computer" has been highlighted by Quantum Zeitgeist.
quantumzeitgeist.com/researchers-...

‘Virtual cell’ captures most-basic process of life: bacterial division Researchers simulated nearly every molecule in a bacterial cell — and then watched the cell grow and reproduce.

#compbio Good read: Virtual cell’ captures most-basic process of life: bacterial division www.nature.com/articles/d41...

Experimental Realization of the Markov Chain Monte Carlo Algorithm on a Quantum Computer
https://arxiv.org/pdf/2603.08395
Baptiste Claudon, Sergi Ramos-Calderer, Jean-Philip Piquemal.

Baptiste Claudon, Sergi Ramos-Calderer, Jean-Philip Piquemal: Experimental Realization of the Markov Chain Monte Carlo Algorithm on a Quantum Computer https://arxiv.org/abs/2603.08395 https://arxiv.org/pdf/2603.08395 https://arxiv.org/html/2603.08395

#quantumcomputing New preprint: "Experimental Realization of the Markov Chain Monte Carlo Algorithm on a Quantum Computer". We experimentally encoded & accurately ran a quantum MCMC on the H2 & Helios quantum computers. @qubit-pharma.bsky.social @quantumlah.bsky.social
arxiv.org/abs/2603.08395

Assessing Boltz-2 Performance for the Binding Classification of Docking Hits The recently released Boltz-2 cofolding model is generating high expectations by enabling both protein–ligand structure and binding affinity predictions. When applied to a recently described and chall...

#compchem #compchemsky #biosky
Good read: Assessing Boltz-2 Performance for the Binding Classification of Docking Hits pubs.acs.org/doi/10.1021/...

#compchem #compchemsky Our paper in J. Phys. Chem. Lett.: "Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation" made it to one of the covers! pubs.acs.org/doi/full/10....

Quantum computers will finally be useful: what’s behind the revolution A string of surprising advances suggests usable quantum computers could be here in a decade.

#quantumcomputing Good read: Quantum computers will finally be useful: what’s behind the revolution
www.nature.com/articles/d41...

🚀 Game-changing speed for drug discovery simulations without trading accuracy for Relative Binding Free Energy (RBFE) calculations.
Dual-LAO delivers 15–30× faster simulations while maintaining industry-leading accuracy (~0.5–0.6 kcal/mol). #compchem
t.co/dDLVqXKvZm

🚀First paper published!
We introduce DMTS, a multi-time-step method for ML force fields
✔️×4 speed-up
✔️Accuracy preserved
✔️Generalizable to any ML potential
📄Link: pubs.acs.org/doi/full/10....
The preprint: arxiv.org/abs/2510.06562
@jppiquem.bsky.social
#MolecularDynamics #MachineLearning

🤩 New year, new publication using the FeNNix-Bio1 foundation model !

🚀« Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation» published in the Journal of Physical Chemistry Letters
#compchemsky #biosky #machinelearning

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models Using Multiple Time Steps and Distillation We present a distilled multi-time-step (DMTS) strategy to accelerate molecular dynamics simulations using foundation neural network models. DMTS uses a dual-level neural network, where the target accurate potential is coupled to a simpler but faster model obtained via a distillation process. The 3.5 Å cutoff distilled model is sufficient to capture the fast-varying forces, i.e., mainly bonded interactions, from the accurate potential, allowing its use in a reversible reference system propagator algorithm (RESPA)-like formalism. The approach conserves accuracy, preserving both static and dynamic properties, while enabling us to evaluate the costly model only every 3 to 6 fs depending on the system. Consequently, large simulation speedups over standard 1 fs integration are observed: nearly 4-fold in homogeneous systems and 3-fold in large solvated proteins through leveraging active learning for enhanced stability. Such a strategy is applicable to any neural network potential and reduces the performance gap with classical force fields.

#compchem #machinelearning
1st of the year in J. Phys. Chem. Lett.: "Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation". pubs.acs.org/doi/full/10....
(see also the updated preprint: arxiv.org/abs/2510.06562)

Point of no returns: researchers are crossing a threshold in the fight for funding With so little money to go round, the costs of competing for grants can exceed what the grants are worth. When that happens, nobody wins.

Good read: www.nature.com/articles/d41...

Cheers to 2026! Happy new year everyone.

#hpc #supercomputing #machinelearning #compchem
New Grand Challenges @gencifrance.bsky.social report dedicated to the Jean Zay 4 machine at IDRIS. Our work on the FeNNix-Bio1 machine learning foundation model can be found on pages 22-25.
genci.fr/sites/defaul...

Fast Parametrization of Martini3 Models for Fragments and Small Molecules Coarse-grained molecular dynamics simulations, such as those performed with the recently parametrized Martini 3 force field, simplify molecular models and enable the study of larger systems over longer time scales. With this new implementation, Martini 3 allows more bead types and sizes, becoming more amenable to studying dynamical phenomena involving small molecules such as protein–ligand interactions and membrane permeation. However, while solutions existed to automatically model small molecules using the previous iteration of the Martini force field, there is no simple way to generate such molecules for Martini 3 yet. Here, we introduce Auto-MartiniM3, an advanced and updated version of the Auto-Martini program designed to automate the coarse-graining of small molecules to be used with the Martini 3 force field. We validated our approach by modeling 81 simple molecules from the Martini Database and comparing their structural and thermodynamic properties with those obtained from models designed by Martini experts. Additionally, we assessed the behavior of Auto-MartiniM3-generated models by calculating solute translocation and free energy across lipid bilayers. We also evaluated more complex molecules such as caffeine by testing its binding to the adenosine A2A receptor. Finally, our results from deploying Auto-MartiniM3 on a large data set of molecular fragments demonstrate that this program can become a tool of choice for fast, high-throughput creation of coarse-grained models of small molecules, offering a good balance between automation and accuracy. Auto-MartiniM3 source code is freely available at https://github.com/Martini-Force-Field-Initiative/Automartini_M3.

#compchem #compbio Good read: Fast Parametrization of Martini3 Models for Fragments and Small Molecules pubs.acs.org/doi/10.1021/...

Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computatio...

#compchem Good read: Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials pubs.acs.org/doi/10.1021/...

Merry Christmas!!!

#compchem #compbio Last preprint of the year: "Fast, systematic and robust relative binding free energies for simple and complex transformations : dual-LAO".
arxiv.org/abs/2512.17624
Great work by N. Ansari. @qubit-pharma.bsky.social .
Another nice collab with J. Hénin.

Wishing you happy holidays. See you in 2026!!! #compchem

Also, if you check the Github, FeNNol can also launch MACE, MACE-OFF and ANI simulations. Enjoy! #compchem

A Foundation Model for Accurate Atomistic Simulations in Drug Design While artificial intelligence has revolutionized the prediction of static protein structures, characterizing their dynamics and interactions with drug candidates remains a computational bottleneck. He...

You can also check the updated version of the preprint that includes a unified transformers architecture as well as the full computation of the Freesolv hydration free energies dataset etc... #compchem #compbio
doi.org/10.26434/che...

GitHub - FeNNol-tools/FeNNol-PMC: FeNNol Pretrained Models Collection FeNNol Pretrained Models Collection. Contribute to FeNNol-tools/FeNNol-PMC development by creating an account on GitHub.

💫 We just released the weights of the #FeNNixBio1 foundation machine learning model for drug design! 💫

Weights: github.com/FeNNol-tools...
FeNNol code: github.com/FeNNol-tools...
The models are distributed under the open source ASL license (non-commercial academic research). #compchem #compbio

A Foundation Model for Accurate Atomistic Simulations in Drug Design While artificial intelligence has revolutionized the prediction of static protein structures, characterizing their dynamics and interactions with drug candidates remains a computational bottleneck. He...

You can also check the updated version of the preprint that includes a unified transformers architecture as well as the full computation of the Freesolv hydration free energies dataset etc...
doi.org/10.26434/che...