@acellera

Computable Drug Discovery. Our mission is to transform drug discovery into a computable task.

 USA, Spain
acellera.com
Joined March 2011
  • Tweets1.6K
  • Following142
  • Followers1.1K
480Media
Preprint release 😀 of "Speak to a Protein," an AI co-scientist that facilitates data gathering and analysis in an interactive collaborative session. It is quite amazing to use. Preprint: arxiv.org/abs/2510.17826
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ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery #DrugDiscovery pubs.acs.org/doi/10.1021/acs… @11carlesnavarro @MacMaje @gtresadern @VincentMoens @gdefabritiis @acellera #JCIM Vol64 Issue15 #MachineLearning #DeepLearning

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery

Abstract. In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecu

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Acellera retweeted
Acellera@acellera13 Jun 2024
Acellera's CEO @gdefabritiis will give a lecture today on machine learning potentials at portal.ml4dd.com/summerschoo…
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Exciting News! 🚀 We've updated open.playmolecule.org with enhancements including: 🕜Reduced waiting time on viewer launch ⚡ Faster file loading 📊 Support for larger CSV tables 🔎 Options to filter table content 🖼️ Improved representation rendering Check it out now!
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NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics #MolecularDynamics #MachineLearning pubs.acs.org/doi/10.1021/acs… @jchodera @gdefabritiis @openmm_toolkit @acellera #JCIM Vol63 Issue18 #ApplicationNotes

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular...

Abstract. Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constraine

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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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We finally come to the end of @PharmaSummer School at @unipr A special thanks to @acellera, to sponsor the Best Presentation Award and congratulations to the winner, Lorenzo Tagliazucchi from @UNIMORE_univ
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Allosteric modulation at @PharmaSummer School with Sofia Oliveira from @BristolUni and Elisa Donati from @acellera
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Day 2 underway, well done to the well-brewed heads that arrived on time! #CBMC23 #conferencehangover
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A great first day of talks at #CBMC23 Computational Biomedicine has come a long way since CBMC19!
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Our paper on OpenMM's highly accelerated support for using hybrid ML/MM potentials like ANI2x is out! pubs.acs.org/doi/10.1021/acs… With Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Tom Markland, and @gdefabritiis

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular...

Abstract. Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constraine

pubs.acs.org
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Build your molecular systems for MD simulation with SystemBuilder! 🔧🏗️ Create complex systems with protein, ligands, and membrane effortlessly. Unlock new possibilities at open.playmolecule.com! #MDsimulation
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Simplify your MD simulations with SimpleRun! 🏃‍♂️⚡️ Perform equilibration and production runs on systems created with SystemBuilder. Unleash the power of molecular dynamics at open.playmolecule.com!
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Get your protein ready for molecular dynamics simulations with ProteinPrepare!💻 Prepare proteins and other systems, including nucleic acids, for accurate simulations.🧬 Try the new version at open.playmolecule.com! #MolecularDynamics
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Get accurate small molecule parameters in a flash with Parameterize! ⚡️ Assign parameters and fix dihedral angles using neural network potentials. Explore Parameterize at open.playmolecule.com!
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Build the perfect membrane for your membrane protein simulations with MembraneBuilder! 💻 Create custom bilayer membranes with precise control over size, lipid composition, and lipid ratio. Try the new version at open.playmolecule.com!
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Harness the potential of machine learning with KdeepTrainer! 🚀 Train a Kdeep binding affinity predictor model using your own dataset. Use the model in the Kdeep app to evaluate binding affinity for new protein-ligand conformations.
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Discover the power of our network-based affinity predictor in Kdeep! 🔍🌟 Enter a set of ligands docked in a protein and let Kdeep accurately predict their binding affinities. Try the new version at open.playmolecule.com!
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Fuel your quest for novel drug candidates with Generative! 💡Generate new ligands from an existing SDF structure by applying chemically reasonable mutations. Explore different modes like mutate, grow, and link. Try the new version at open.playmolecule.com!
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Unleash the predictive power of DeepSite in your quest for drug discovery! 🎯 Identify ligand binding pockets in your protein of interest using state-of-the-art neural network predictions. Try the new version at open.playmolecule.com!
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