I am happy to share a review I recently wrote on the design of peptide binders. It gives an overview of experimentally validated tools and discusses the challenges of why peptide design is more difficult than the design of classical protein binders.
www.chimia.ch/chimia/artic...
In the W-S lab's first preprint, we describe how genomic language models know something about RNA thermodynamics. Though we think this is cool, things get tricky!
A growing practice for interpreting LMs is to perturb input tokens, often called "Categorical Jacobian": 👇
Now published in NSMB!
Paper: doi.org/10.1038/s415...
Full PDF: rdcu.be/fhBtI
Overview of additions since the preprint👇 (1/5)
Can proteins fold and function with half of the amino acid alphabet?
Using only 10 residues, we designed stable, mutation-resilient structures—no aromatics or basics involved.
A minimalist foundation for ancient biology and synthetic design. tinyurl.com/37t8br4v
#ProteinDesign #OriginsOfLife
My time in @martinsteinegger.bsky.social's group is ending, but I’m staying in Korea to build a lab at Sungkyunkwan University School of Medicine. If you or someone you know is interested in molecular machine learning and open-source bioinformatics, please reach out. I am hiring!
mirdita.org
Fantastic analysis from the OpenADMET team (Maria Castellanos, Hugo MacDermott-Opeskin) showing that the zero-shot ADMET models ADMETlab 3.0 and ADMET-AI generalize poorly to their recent OpenADMET-ExpansionRx Blind Challenge data openadmet.ghost.io/zero-shot-ex...
@martinpacesa.bsky.social's group crushed the latest Adaptyv competition: 3/7 hit rate v.s. average of 2.8% (!). congratulations also to the other groups with confirmed binders -- looks like a very hard target
We have started a project trying to predic the interactions/structures of all yeast protein pairs using an AlphaFold pooling approach. We are making the current dataset open and we welcome collaborations.
www.evocellnet.com/2026/03/mapp...
We are excited to announce the early-stage release of our S. cerevisiae structural interactome mapping project. Using AlphaFold3 (AF3), w...
Is #AI hitting a plateau in structure prediction? Help us find out at CASP17! 🧪🧬
Calling for Targets: Immune Complexes, protein - ligand complexes, RNA/DNA, conformational ensembles, membrane proteins, viral origins, and large complexes.
The Rule of Thumb: If AF3 can’t model it, we want it.
Can we simulate realistic evolutionary trajectories and “replay the tape of life”? In this work, we propose a flexible, generalizable deep learning framework for modeling how the entire protein sequence evolves over time while capturing complex interactions across sites. 1/n
doi.org/10.64898/202...
This work introduces the Runs N’ Poses dataset for benchmarking deep learning methods on the protein–ligand complex prediction task. It shows that current methods rely on memorization, challenging the...
Excited to share our latest preprint evaluating AlphaFold3, Boltz-1, Chai-1 and Protenix for predicting protein-ligand interactions, featuring our newly introduced benchmark dataset 🌹Runs N’ Poses🌹!
www.biorxiv.org/content/10.1...
🧵👇 (1/n)
Torsten Schwede
Yun S. Song
Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with other molecules, especially small mo...
Early proteins likely arose from a chemically limited set of amino acids available through prebiotic chemistry, raising a central question in molecular evolution: could such primitive compositions yie...
Maria Castellanos Hugo MacDermott-Opeskin
It’s been more than a month since the OpenADMET-ExpansionRx challenge wrapped up, but the conversation is just getting started. Launched on October 27, 2025...
