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🚀 ACEGEN by Acellera Therapeutics Our new toolkit for generative drug design that harnesses #ReinforcementLearning. Designed for robustness and efficiency, ACEGEN accelerates the discovery of desirable molecules. Learn more 👉 acellera.com/blog/acegen-a-… #DrugDiscovery #AI #Pharma

Discover how Acellera Tx is leveraging AI-driven drug discovery to deliver efficient target-to-hit insights within weeks, achieving a hit rate of over 10% across various target families with our cutting-edge technology. 📅 Book a meeting to learn more: login.partnering.bio.org/inova-business…

Trying to build nice graphics for a presentation? We are always working at improving the #PlayMolecule viewer. Try lnkd.in/dhVwfp7x.

Acellera's CEO Gianni De Fabritiis will give a lecture today on machine learning potentials at portal.ml4dd.com/summerschool

#ACEFORCE coming along nicely. Scan with the Aceforce DFT model on the Sellers torsion scan benchmark and we are still improving it.

We are thrilled to announce that Professor Andrew White (Andrew White 🐦‍⬛), an AI-driven drug discovery pioneer, has joined Acellera Therapeutics' Advisory Board. Welcome, Andrew! acellera.com/blog/prof-andr…

Gianni De Fabritiis is attending the L2M3 Workshop at CECAM-HQ in Lausanne! He'll showcase as well Acellera's work on applying chemical language models for chemistry. Stay tuned for insights and updates! #LLMs #Chemistry #Acellera #Innovation @CECAM_Org Gianni De Fabritiis

ACEGEN is now oficially published in JCIM & JCTC Journals. Check it out! ➡️ pubs.acs.org/doi/10.1021/ac…

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery #DrugDiscovery pubs.acs.org/doi/10.1021/ac… @11carlesnavarro Maciej Majewski Gary Tresadern vmoens Gianni De Fabritiis Acellera Therapeutics #JCIM Vol64 Issue15 #MachineLearning #DeepLearning

This afternoon, our CEO, Gianni De Fabritiis , will be speaking at the @CECAMevents Flagship WS on how to enhance  the accuracy of relative binding free energies via machine learning potentials

Great news! Our new PlayMolecule drug discovery protocol identified 2 compounds with nanomolar activity (40 & 85 nM) for RIPK2 and >1000 selectivity vs RIPK1. With a 40% hit rate and a 1-week turnaround, we're pushing the limits of rapid, AI-driven discovery.

Recent results from our new QuantumBind-ABFE protocol showed strong alignment with experimental outcomes in our latest RIPK2 campaign, with prediction errors of 0.6 and 1.7 kcal/mol for our two nanoMolar hits, ranking them at the top of the list.

Curious about our tools for drug discovery? Join us on Nov 6 at CCPBioSim's Industry Talk, where we'll discuss how we use MD simulations in our QuantumBind platform. Register for free here: ukri.zoom.us/webinar/regist…

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: acellera.com/blog/release-o…

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/advancing…

Moving forward towards reasoning models