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Acellera
@acellera.com
670 followers 780 following 8 posts
AI-Driven Drug Engineering: Building a discovery factory. www.acellera.com
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Acellera @acellera.com · Jul 24
We’re announcing the publication of our latest research on a universal, machine-learned coarse-grained protein force field: www.nature.com/articles/s41...

The approach demonstrates chemical transferability on diverse proteins, predicting metastable states, and relative folding free energies.
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 combining deep-learning m...
www.nature.com
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acellera.com
Acellera @acellera.com · Jul 24
We’re announcing the publication of our latest research on a universal, machine-learned coarse-grained protein force field: www.nature.com/articles/s41...

The approach demonstrates chemical transferability on diverse proteins, predicting metastable states, and relative folding free energies.
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 combining deep-learning m...
www.nature.com
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acellera.com
Acellera @acellera.com · Jan 29
Acellera Therapeutics and Psivant Therapeutics to collaborate on transformative computational drug discovery, leveraging AI, quantum simulations, and physics-based methods to accelerate small molecule design.
Learn more: www.acellera.com/blog/aceller...
Acellera and Psivant Collaborate to Develop Transformative Computational Drug Discovery Approaches Using AI and Quantum Simulations - Acellera Blog
www.acellera.com
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acellera.com
Acellera @acellera.com · Jan 15
Learn how to set up and execute protein-ligand simulations with AceForce in our step-by-step tutorial:
software.acellera.com/acemd/nnpmm....
NNP/MM simulations — Acellera documentation
software.acellera.com
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acellera.com
Acellera @acellera.com · Jan 15
AceForce 1.0 isn’t just for RBFE calculations—it’s also enabling advanced protein-ligand simulations using NNP/MM methodologies.

Discover how AceForce enhances accuracy and efficiency in computational drug discovery.
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acellera.com
Acellera @acellera.com · Jan 7
AceForce 1.0 is here.

Our neural network potential is designed to enhance drug discovery with quantum-level accuracy. AceForce 1.0 delivers precise predictions, reduces computational costs, and integrates seamlessly into workflows.

Discover more: acellera.com/blog/advanci...

#compchemsky
Advancing Drug Discovery with AceForce 1.0 - Acellera Blog
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acellera.com
Acellera @acellera.com · Dec 4
ACEMD 4.0.0 is here! Now featuring seamless integration of Neural Network Potentials (NNP) with Molecular Mechanics (MM) for advanced hybrid simulations.

Key features: 🔹 Simplified NNP/MM simulations 🔹 Python API & backward compatibility

Explore more: www.acellera.com/blog/release...
Release of ACEMD 4.0.0: Advancing Machine Learning for Molecular Dynamics Simulations - Acellera Blog
www.acellera.com
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acellera.com
Acellera @acellera.com · Nov 21
We recently presented our drug discovery methods in a CCPBioSim Industry Talk. Adrià Pérez, one of our computational chemists, showcased how MD simulations power conformational analysis, pose prediction, and free energy calculations.
Watch here: www.acellera.com/blog/aceller...
Acellera CCPBioSim IndustryTalk: Applications of MD Simulations in Drug Discovery Pipelines - Acellera Blog
www.acellera.com
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acellera.com
Acellera @acellera.com · Nov 20
New work on neural network potentials for lightly coarse-grained systems. AMARO models.
gdefabritiis.bsky.social
Gianni De Fabritiis @gdefabritiis.bsky.social · Nov 19
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
A novel machine-learning-based force field for protein thermodynamics. It uses an all-heavy-atom approach without hydrogens to simplify simulations while maintaining key dynamics.
pubs.acs.org/doi/10.1021/...
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