Good Papers

Showing papers from Max Planck Institute of Biochemistry Show all papers

45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Max Planck Institute of BiochemiMax Planck Institute for BiochemHKUST-GZETH ZurichLudwig-Maximilians-Universität MProteins

Joint protein, mRNA, DNA sequence design and optimization with nucleotide-level Potts models

Blazej Banaszewski, Lars J Dornfeld, Dexiong Chen, Karsten Borgwardt and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Max Planck Institute of BiochemiDepartment of Machine Learning aMPI of BiochemistryHKUST-GZETH ZurichVision backbones

Channel Mixer: A Pretrainable Tokenizer for Scalable Multi-Channel Vision Transformers

Lukas Miklautz, Lucas Miranda, Nikola Bulat, Armin Lambacher and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
86%Must read
?Must readVote to see the score
NeurIPS 2026Max Planck Institute of BiochemiMicrosoft ResearchMolecules & drug discovery

Monroe: A Molecular Foundation Model for In-context Probabilistic Inference

Monroe is a molecular foundation model pre-trained on 81 million molecules that uses in-context TabPFN prediction to achieve state-of-the-art bioassay activity prediction, especially on activity cliffs.

Blazej Banaszewski, Andrew Fitzgibbon

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 2/5