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phseidl.bsky.social

@phseidl.bsky.social
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schimunek.bsky.social @schimunek.bsky.social · 13/05/2025
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/... 🖥️ App: huggingface.co/spaces/ml-jk...
pubs.acs.org
MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery
Today’s drug discovery increasingly relies on computational and machine learning approaches to identify novel candidates, yet data scarcity remains a significant challenge. To address this limitation,...
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Reposted by @phseidl.bsky.social
Sebastian Sanokowski @sanokows.bsky.social · 24/04/2025
1/11 Excited to present our latest work "Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics" at #ICLR2025 on Fri 25 Apr at 10 am! #CombinatorialOptimization #StatisticalPhysics #DiffusionModels
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Reposted by @phseidl.bsky.social
Günter Klambauer @gklambauer.bsky.social · 03/12/2024
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/
moleculediscovery.github.io
ML for molecules and materials in the era of LLMs [ML4Molecules]
ELLIS workshop, HYBRID, December 6, 2024
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Reposted by @phseidl.bsky.social
Kevin K. Yang 楊凱筌 @kevinkaichuang.bsky.social · 20/11/2024
Long-context xLSTM models of DNA, proteins, and chemicals. @smdrnks.bsky.social @phseidl.bsky.social @gklambauer.bsky.social arxiv.org/abs/2411.04165
Overview of Bio-xLSTM Conditional generation of molecules via ICL and 15M parameter modelGenerative pre-training of protein language models
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