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🚨 Check out 🧬 Bio-xLSTM: xLSTM for biological domains: DNA, proteins, and small molecules ✅ Generative model ✅ In-context learning ✅ Representation learning 🧑‍💻 Code on Github available

The Machine Learning for Molecules workshop 2024 will take place THIS FRIDAY, December 6. Tickets for in-person participation are "SOLD" OUT. We still have a few free tickets for online/virtual participation! Registration link here: moleculediscovery.github.io/workshop2024/

Thanks to all participants, contributors, Organizers, and .. speakers of ML4MOLECULES 2024! We'll be back!

Another great #Ellis workshop on ML for Molecular Discovery. Thanks to Günter Klambauer, Francesca Grisoni, and the whole organizing team! Proud of our team's contributions - 3 posters (Daniel Armstrong, Anna Kelmanson, Zlatko Jončev) and an excellent talk by Sarina Kopf! Very well done! #compchem

I am excited to present the result of a fruitful internship at AI at Meta. We introduce preference discerning, which denotes in-context conditioning on user preferences expressed in text to steer the recommendation system. This enhances flexibility and personalization. 1/n

Are you using generative #DeepLearning for de novo molecule design?🧪 🖥️ Then check out Rıza Özçelik ‘s latest work, where we perform a (super) large scale analysis (~1 B designs!) & find ‘traps’, ‘treasures’ and ‘ways out’ in the jungle of generative drug discovery. 🌴 🐒 👇

Accepted at ICLR2025! See you in Singapore!! #ICLR2025

Thrilled to announce that our work, LiveXiv, has been accepted to #ICLR2025 ! 🌟 Introducing LiveXiv—a challenging, maintainable, and contamination-free scientific multi-modal live dataset, designed to set a new benchmark for Large Multimodal Models (LMMs). 🚀🙌

Join my team as PhD or PostDoc researcher!!!

Happy to finally release our work on "Composing Unbalanced Flows for Flexible Docking and Relaxation" (FlexDock) that we will be presenting as an oral at #ICLR2025 ! 🤗✈️🇸🇬 A thread! 🧵

Equivariant functions (e.g. GNNs) can't break symmetries, which can be problematic for generative models and beyond. Come to poster #207 Saturday at 10AM to hear about our solution: SymPE, or symmetry-breaking positional encodings! w/Vasco Portilheiro, Yan Zhang, Oumar Kaba

MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery Few-shot models for molecules now easily accessible via web application. Predictions via prompting SMILES P: pubs.acs.org/doi/10.1021/ac…

Need to predict bioactivity 🧪 but only have limited data ❌? Try our interactive app for prompting MHNfs — a state-of-the-art model for few-shot molecule–property prediction. No coding or training needed. 🚀 📄 Paper: pubs.acs.org/doi/10.1021/ac… 🖥️ App: huggingface.co/spaces/ml-jku/…

Talking at CAIML Symposium today "A New Generation of Foundation Models Based on xLSTM" Slides available here: cloud.ml.jku.at/s/fLtjm5GKQSzR… caiml.org/news/194/

Happy to introduce 🔥LaM-SLidE🔥! We show how trajectories of spatial dynamical systems can be modeled in latent space by --> leveraging IDENTIFIERS. 📚Paper: arxiv.org/abs/2502.12128 💻Code: github.com/ml-jku/LaM-SLi… 📝Blog: ml-jku.github.io/LaM-SLidE/ 1/n

MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery #DrugDiscovery pubs.acs.org/doi/10.1021/ac… Johannes Schimunek Sohvi Luukkonen Günter Klambauer LIT AI Lab & ELLIS Unit Linz #JCIM Vol65 Issue9 #ApplicationNote

A European-developed TiRex is leading the field—significantly ahead of U.S. competitors like Amazon, Datadog, Salesforce, and Google, as well as Chinese models from companies such as Alibaba.

📢 Present your NeurIPS paper in Europe! Join EurIPS 2025 + ELLIS UnConference in Copenhagen for in-person talks, posters, workshops and more. Registration opens soon; save the date: 📅 Dec 2–7, 2025 📍 Copenhagen 🇩🇰 🔗eurips.cc #EurIPS EurIPS Conference

Machine Learning-Driven Optimization of Specific, Compact, and Efficient Base Editors via Single-Round Diversification New base editors designed and optimized with ML! Almost all ML-designed ones active in the lab tests!! P: biorxiv.org/content/10.110…