Clay Kosonocky

@kosonocky.bsky.social

ML + Biochemistry PhD Candidate at UT Austin. BioML Society Founder. All problems are solvable, so let's solve some biomlsociety.org

I'll be in Seoul next week for ICML 2026! 🇰🇷 Message me if you'd like to meet up for food, coffee, or drinks! Would love to talk all things proteins, small molecules, and biology in general 🧬

I’ve been asked a few times how I got started running a worldwide protein design competition. The short answer is that I never intended to start this project at all: it just sort of happened. The more correct answer requires me to tell the origin story of the BioML Society [1/2]

The results are finally in! 🏆💻🧬 I'm thrilled to announce that the manuscript for the Bits to Binders protein design competition is out on bioRxiv! Here's a summary of our findings, including some simple criteria that nearly *double* success rates when applied as a filter 🧵

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🚨The Bits to Binders Competition has concluded!🧬 One year ago we gathered scientists from around the world to design and submit protein binders that cause immune cells to target and eliminate CD20+ tumors Spoiler: They work!

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Some recent used bookstore acquisitions of mine Part of a few themes I'm trying to understand: - cultures & life via classic lit - basics of philosophy & history - modern political climate & trajectory - how can we make cities better? - what will the future look like?

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PSA: Digging into Chai-1 and found out that it gives different protein-ligand structures & confidences depending on the SMILES canonicalization used May also be the case with AF3 & Boltz-1, but haven't verified As to what this means... see the rest of the thread 🧵

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This year I've read 30 books so far. Here are the ~10 that I found the most valuable 🧵 1) One Hundred Years of Solitude (1967) by Gabriel Garcia Marquez Fantastic novel that I think everybody should read once. Captures the feeling of human life that feels instinctually correct

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Last year I made an intro Protein + ML project to teach BioML Society members about protein embeddings. Here it is! Hope you all enjoy Should get you familiar extracting embeddings for proteins, visualizing them, and using them for property prediction

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Has anybody been curating a list of the most seminal papers in the BioML space? Mainly interested in work that expands our ability to do and understand new things rather than improve upon benchmarks