@MacMaje

PhD in comp. chem. Currently @sanofi. Previously in @acellera, @gdefabritiis, @XavierBarril and @JemielityLab labs He/him

 Barcelona
Joined March 2021
  • Tweets49
  • Following190
  • Followers93
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I’m happy to share our recent preprint on using neural network potentials (NNPs) for coarse-grained molecular dynamics (CGMD) simulations of proteins. Here's a summary of our findings: 🧵1/8 arxiv.org/abs/2212.07492

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern...

arxiv.org
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Christian Dallago@sacdallagoOct 14
The 2027/2028 NVIDIA Graduate Fellowship is open! PhD students can receive up to $60,000, plus a summer internship with NVIDIA. I want to especially encourage life science students to apply! Applications and recommendation letters are due October 30. research.nvidia.com/graduate…
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Cecilia Clementi@CecClementi18 Jul 2025
Our development of machine-learned transferable coarse-grained models in now on Nat Chem! doi.org/10.1038/s41557-025-0… I am so proud of my group for this work! Particularly first authors Nick Charron, Klara Bonneau, @sayeg84, Andrea Guljas.

Navigating protein landscapes with a machine-learned transferable coarse-grained model

Nature Chemistry - The development of a universal protein coarse-grained model has been a long-standing challenge. A coarse-grained model with chemical transferability has now been developed by...

nature.com
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Christian Dallago@sacdallago3 Jun 2025
Replying to @HeinzingerM
@HeinzingerM and I are seeking talented postdocs to support for the Marie Skłodowska-Curie Fellowship! Join our international AI+biology team, collaborate on protein design, and access top labs in the US & EU. Interested? Apply by July 15! Details: machine.learning.bio/news/ms…

Marie Skłodowska-Curie Fellowship — machine.learning.bio

Archived posting: a postdoctoral opportunity in AI for protein science.

machine.learning.bio
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Christian Dallago@sacdallago15 Nov 2024
GPUs are *fast* at aligning protein sequences (and profiles!). 178x faster than JackHMMER! In ColabFold, 23x faster end-to-end compared to AlphaFold2 reaching the same accuracy! You get immediate speedups for all methods leveraging MSAs, from DCA, to PoET, and even AlphaFold3!
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Christian Dallago@sacdallago18 Jul 2024
ml4lms.bio will be next friday. We haveso many quality submissions -- come meet the authors! Likewise, we have a fantastic lineup, prizes, food,... Everything to set you up for the next exciting paper! Also, sign up for the social ASAP: lu.ma/rojc95k1
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Christian Dallago@sacdallago10 May 2024
🏳️🏳️🏳️🏳️🏳️🏳️ OK, ok; my bad! ml4lms.bio submission deadline extended to May 23rd. Remember: it's **UP TO** 5 pages. Subit any below that and you're fine! Just make it sensible, topical & exciting.
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In #ACEGEN, we take advantage of the #TorchRL library of #PyTorch to do generative chemistry. It implements #REINFORCE, #REINVENT, #A2C, #PPO, #PPOD etc. A solid base for #RL in drug discovery. Code available under MIT. @acellera arxiv.org/abs/2405.04657

ACEGEN: Reinforcement learning of generative chemical agents for...

In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a...

arxiv.org
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Great opportunity for fresh BSc graduates to do an internship at Sanofi in Barcelona! Feel free to reach out if you have any questions.
Sanofi España
@SanofiES12 Apr 2024
📣¡Llega el #SanofiGraduateProgram 2024-25 con 50 vacantes para roles digitales y empresariales! Cristina quiere dar un consejo a l@s recién graduad@s que queréis comenzar vuestra carrera profesional en una farmacéutica líder. 🔗bit.ly/4d7YCW3 #SanofiEsTalento
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Acellera@acellera25 Oct 2023
We are thrilled to share our new publication pubs.acs.org/doi/full/10.102… 📚 Using only protein structures and short trajectories, we have trained coarse-grained NNPs capable of maintaining native structures and folding proteins. Thanks to @11carlesnavarro, @MacMaje and @gdefabritiis!

Top-Down Machine Learning of Coarse-Grained Protein Force Fields

Abstract. Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactio

pubs.acs.org
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Acellera@acellera29 Oct 2023
Excited to share our latest review article on advancing the frontier of #DrugDiscovery through #MachineLearning! We delve into ML algorithms for predicting molecular properties of small molecules, a key step towards computable drug discovery. 🧪🖥️ 🔗 doi.org/10.1016/j.aichem.202…
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Cecilia Clementi@CecClementi30 Oct 2023
I am excited to present this work, result of a 4-year big collaborative project: arxiv.org/abs/2310.18278 #MachineLearning a transferable bottom-up protein force field, trained on force data from over all-atom MD simulations, using physical priors and graph neural networks.🧵⬇️

Navigating protein landscapes with a machine-learned transferable...

The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal,...

arxiv.org
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👩‍💻 TorchMD: A Deep Learning Framework for Molecular Simulations #JCTC #OpenAccess 🔓 go.acs.org/6iV #MolecularMechanics 📚🔬🖥️
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Happy to share that my latest paper was published in @NatureComms ! 🥳 #MolecularDynamics #MachineLearning #CoarseGraining #MD #ML #NNP #NatureComms #BiotechNatureComms
Nature Communications@NatureComms22 Sep 2023
MacMaje @gdefabritiis @FrankNoeBerlin @CecClementi @acellera construct coarse-grained molecular potentials using artificial neural networks, accelerating protein dynamics simulations while preserving their thermodynamics. #BiotechNatureComms nature.com/articles/s41467-0…
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Maciej Majewski retweeted
Qian Cheng@sgqcheng16 Sep 2023
⁦@MacMaje⁩ ⁦@gdefabritiis⁩ @CecClementi ⁦@acellera⁩ present machine learning coarse-grained potentials of protein thermodynamics. ⁦@NatureComms⁩ #moleculardynamics #coarsegraining #BiotechNatureComms nature.com/articles/s41467-0…

Machine learning coarse-grained potentials of protein thermodynamics

Nature Communications - Understanding protein dynamics is a complex scientific challenge. Here, authors construct coarse-grained molecular potentials using artificial neural networks, significantly...

nature.com
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Maciej Majewski@MacMaje27 Aug 2023
We're expanding our AI team specialising in Biologics. Contact me if you are looking for a job. #Sanofi #job #biologics #ML
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I have some news! After 2 amazing years at #Acellera, I'm starting a new chapter at #Sanofi as a Computational Scientist. Excited to chase the miracles of science to improve people's lives! 🚀 #PursueProgress #DiscoverExtraordinary
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We're expanding our AI team specialising in Biologics. Contact me if you are looking for a job. #Sanofi #job #biologics #ML
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Diego del Alamo@DdelAlamo22 Jun 2023
"Top-down machine learning of coarse-grained protein force-fields" General-purpose neural network potentials trained on 15,000 proteins are compared to protein-specific potentials arxiv.org/abs/2306.11375
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Carles Navarro
@11krls27 Jun 2023
1/🧵 Happy to share my first preprint! "Top-down machine learning of coarse-grained protein force-fields" We've trained a general and protein specific coarse-grained neural network potentials (NNPs) using only protein structures and short molecular dynamic trajectories.
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Maciej Majewski retweeted
OpenMM@openmm_toolkit3 Jun 2023
You can find the ATMMetaForce OpenMM plugin here! github.com/Gallicchio-Lab/op…

GitHub - Gallicchio-Lab/openmm-atmmetaforce-plugin: An OpenMM plugin that implements the Alchemical...

An OpenMM plugin that implements the Alchemical Transfer Potential - Gallicchio-Lab/openmm-atmmetaforce-plugin

github.com
JCIM & JCTC Journals@JCIM_JCTC1 Jun 2023
Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series #MolecularSimulations #drugdiscovery pubs.acs.org/doi/10.1021/acs… @MacMaje @GallicchioEmi @gdefabritiis Vol63 Issue8 #JCIM #compchem
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