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Anthony Gitter

@anthonygitter.bsky.social

Computational biologist; Associate Prof. at University of Wisconsin-Madison; Jeanne M. Rowe Chair at Morgridge Institute https://gitterlab.org/

42 Posts90 Follower43 Folgt
Travis Wheeler@wheelerlab.org · 3w ago

Thanks for the advert, @martinsteinegger.bsky.social. If you're reading this and you're sitting on a pile of molecular dynamics simulations, please consider contributing them to MDRepo. This is the path to AI for dynamics (And if you're wondering: yes, there was only 1 person in the audience! 🤥)

Martin Steinegger 🇺🇦@martinsteinegger.bsky.social · 3w ago

@wheelerlab.org is talking about his effort to build a resource for protein dynamics (PDB for MD). MDRepo is a repository to store simulation data. This is really needed to push the needle in design, function and more. Please help make it successful by sharing your MD data. #ismb2026 🌐 mdrepo.org

Morgridge Institute for Research@morgridgeinstitute.bsky.social · last mo.

A great story stemming from collaboration between @anthonygitter.bsky.social and Nate Wlodarchak, now in Colorado at the Rocky Mountain Regional VA Medical Center. ⬇️

McArdle Laboratory@mcardlelab.bsky.social · last mo.

Former Cancer Biology graduate student Nate Wlodarchak is collaborating with @morgridgeinstitute.bsky.social scientists to find new drugs to treat tuberculosis. Read about it here: morgridge.org/story/unlock...

Hannah Wayment-Steele@hkws.bsky.social · 2mo ago

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": 👇

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Peter Škrinjar@peterskrinjar.bsky.social · 3mo ago

Now published in NSMB! Paper: doi.org/10.1038/s415... Full PDF: rdcu.be/fhBtI Overview of additions since the preprint👇 (1/5)

Evaluating generalization in protein–ligand cofolding methods - Nature Structural & Molecular Biology

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...

doi.org

Peter Škrinjar@peterskrinjar.bsky.social · last yr.

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)

Anthony Gitter@anthonygitter.bsky.social · 4mo ago

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...

Lessons Learned from the OpenADMET-ExpansionRx Blind Challenge: Can We Trust Zero-Shot ADMET Predictions?

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...

openadmet.ghost.io

Torsten Schwede@torstenschwede.bsky.social · 5mo ago

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.

The Critical Assessment of Structure Prediction (CASP) experiment is calling for prediction targets: Immune Complexes, Organic Ligand-Protein Complexes, Nucleic Acids and Complexes, Conformational Ensembles, Difficult Protein Structures and Complexes. Rule of Thumb: If AlphaFold3 can generate a high-quality model, it is likely not a CASP-grade challenge. If it struggles, we want it.
Pedro Beltrao@pedrobeltrao.bsky.social · 5mo ago

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...

Mapping the yeast atructural interactome with AlphaFold3: an open call for collaboration

We are excited to announce the early-stage release of our S. cerevisiae  structural interactome mapping project. Using AlphaFold3 (AF3), w...

evocellnet.com

Klara Hlouchova lab@hlouchova-lab.bsky.social · 9mo ago

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

Ancient amino acid sets enable stable protein folds

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...

tinyurl.com

Max Fürst@maxfus.bsky.social · 8mo ago

New preprint🚨 Imagine (re)designing a protein via inverse folding. AF2 predicts the designed sequence to a structure with pLDDT 94 & you get 1.8 Å RMSD to the input. Perfect design? What if I told u that the structure has 4 solvent-exposed Trp and 3 Pro where a Gly should be? Why to be wary🧵👇

Karthik Anantharaman@karthik-a.bsky.social · 8mo ago

Excited for our new paper on a genome language model for viruses in @natcomms.nature.com: "Protein Set Transformer: a protein-based genome language model to power high-diversity viromics"! Led by PhD student Cody Martin in collaboration with @anthonygitter.bsky.social doi.org/10.1038/s414...

Protein Set Transformer: a protein-based genome language model to power high-diversity viromics - Nature Communications

A genome language model, Protein Set Transformer, trained on viral datasets, uncovers evolutionary rules of protein content and organization driving precise virus identification, host prediction, and ...

doi.org

Anthony Gitter@anthonygitter.bsky.social · 8mo ago

What are good places to post an unsolicited manuscript peer review these days? I don't have a blog. I read manuscripts across arXiv, bioRxiv, ChemRxiv, OpenReview, random white papers, journals, etc. Do I dump it on Zenodo, post it here, and send it to the authors?

Anthony Gitter@anthonygitter.bsky.social · 9mo ago

Our Assay2Mol manuscript was published at EMNLP 2025 doi.org/10.18653/v1/... See the preprint thread below for a summary of the methodology, results, and code. We added more control experiments in this version related to protein sequence identity and generated molecule size.

Anthony Gitter@anthonygitter.bsky.social · last yr.

Our preprint Assay2Mol introduces uses PubChem chemical screening data as context when generating molecules with large language models. It uses assay descriptions and protocols to find relevant assays and that text plus active/inactive molecules as context for generation. 1/

The Assay2Mol workflow. A chemist provides a target description, which is used to retrieve BioAssays from the pre-embedded vector database. After filtering for relevance, the BioAssays are summarized by an LLM. The BioAssay ID is then used to retrieve experimental tables. The final molecule generation prompt is formed by combining the description, summarization, and selected test molecules with associated test outcomes, enabling the LLM to generate relevant active molecules.
Philip Romero@philromero.bsky.social · 10mo ago

AI + physics for protein engineering 🚀 Our collaboration with @anthonygitter.bsky.social is out in Nature Methods! We use synthetic data from molecular modeling to pretrain protein language models. Congrats to Sam Gelman and the team! 🔗 www.nature.com/articles/s41...

Biophysics-based protein language models for protein engineering - Nature Methods

Mutational effect transfer learning (METL) is a protein language model framework that unites machine learning and biophysical modeling. Transformer-based neural networks are pretrained on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics.

nature.com

Anthony Gitter@anthonygitter.bsky.social · 11mo ago

The journal version of "Biophysics-based protein language models for protein engineering" with @philromero.bsky.social is live! Mutational Effect Transfer Learning (METL) is a protein language model trained on biophysical simulations that we use for protein engineering. 1/ doi.org/10.1038/s415...

Biophysics-based protein language models for protein engineering - Nature Methods

Mutational effect transfer learning (METL) is a protein language model framework that unites machine learning and biophysical modeling. Transformer-based neural networks are pretrained on biophysical ...

doi.org

Anthony Gitter@anthonygitter.bsky.social · 12mo ago

The journal version of our paper 'Chemical Language Model Linker: Blending Text and Molecules with Modular Adapters' is out doi.org/10.1021/acs.... ChemLML is a method for text-based conditional molecule generation that uses pretrained text models like SciBERT, Galactica, or T5.

Chemical Language Model Linker: Blending Text and Molecules with Modular Adapters

The development of large language models and multimodal models has enabled the appealing idea of generating novel molecules from text descriptions. Generative modeling would shift the paradigm from relying on large-scale chemical screening to find molecules with desired properties to directly generating those molecules. However, multimodal models combining text and molecules are often trained from scratch, without leveraging existing high-quality pretrained models. Training from scratch consumes more computational resources and prohibits model scaling. In contrast, we propose a lightweight adapter-based strategy named Chemical Language Model Linker (ChemLML). ChemLML blends the two single domain models and obtains conditional molecular generation from text descriptions while still operating in the specialized embedding spaces of the molecular domain. ChemLML can tailor diverse pretrained text models for molecule generation by training relatively few adapter parameters. We find that the choice of molecular representation used within ChemLML, SMILES versus SELFIES, has a strong influence on conditional molecular generation performance. SMILES is often preferable despite not guaranteeing valid molecules. We raise issues in using the entire PubChem data set of molecules and their associated descriptions for evaluating molecule generation and provide a filtered version of the data set as a generation test set. To demonstrate how ChemLML could be used in practice, we generate candidate protein inhibitors and use docking to assess their quality and also generate candidate membrane permeable molecules.

doi.org

Anthony Gitter@anthonygitter.bsky.social · last yr.

Our preprint Assay2Mol introduces uses PubChem chemical screening data as context when generating molecules with large language models. It uses assay descriptions and protocols to find relevant assays and that text plus active/inactive molecules as context for generation. 1/

The Assay2Mol workflow. A chemist provides a target description, which is used to retrieve BioAssays from the pre-embedded vector database. After filtering for relevance, the BioAssays are summarized by an LLM. The BioAssay ID is then used to retrieve experimental tables. The final molecule generation prompt is formed by combining the description, summarization, and selected test molecules with associated test outcomes, enabling the LLM to generate relevant active molecules.