Tim O'Donnell

Tim O'Donnell

@timodonnell.bsky.social

Computational immunologist. ML for science @OpenAthena. Previously @OpenVax @MountSinai @DEShawResearch

116 followers130 following27 posts

Tim O'Donnell reposted

Replying to @sokrypton.org

One thing that helped with was implementing a simulated gameplay. Have Claude run 100s of games, get feedback and update ai.js. It is still doing some silly things... but getting better. Perhaps a future direction would be to let multiple LLM compete with eachother by building better ai.js?🤠 (3/4)

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Tim O'Donnell reposted

I'm super excited to announce the first preprint of my PhD, together with Chenxi Ou and @sokrypton.org! ML has revolutionized protein modeling, but crucial challenges remain. For example, we can't reliably predict complicated protein structures without MSAs, which limits what we can design.

Tim O'Donnell reposted

I’ve been testing BoltzGen a bit recently and while I haven’t done any experimental testing yet, the quality of the software is very clear. It installs, runs, logs everything, has tons of options. Very excited to test out the designs irl!

Hannes Stark@hannes-stark.bsky.social

Excited to release BoltzGen which brings SOTA folding performance to binder design! The best part of this project is collaborating with a broad network of leading wetlabs that test BoltzGen at an unprecedented scale, showing success on many novel targets and pushing the model to its limits!

Newbie antibody design q: if I take a mouse IgG1 antibody and express its VH/VL on a human IgG1 constant framework, how likely is it to still bind the target? Does that kind of thing usually work?

Missed this when it came out this summer - high doses of MHC II neoags in a peptide vaccine can be counterproductive due to a population of FOXP3− CD4+ regulatory cells that *kill* cDC1s. Big implications for peptide cancer vaccines if true www.nature.com/articles/s41...

nature.comNeoantigen-specific cytotoxic Tr1 CD4 T cells suppress cancer immunotherapy - NatureType 1 regulatory T cells (Tr1) represent a major obstacle that compromises naturally occurring and therapeutically induced tumour-specific immunity.

Say I have a deep learning model that runs on pairs of things M(a,b), and I want to distill it into three models X,Y,Z where Z(X(a), Y(b)) approximates M(a,b), and hopefully Z can be shallow and fast. Is that problem studied? What should I google?

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