Principal Researcher in BioML at Microsoft Research. He/him/他. 🇹🇼 yangkky.github.io
Kevin K. Yang 楊凱筌
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Screen 1M random protein sequences to discover that biology-like folds are accessible from random sequences with surprising frequency
www.biorxiv.org/content/10.6...
Have you wondered what the wet lab success rates are for current AI-driven protein design models? Look no further!
In our new review, @kevinkaichuang.bsky.social
@avapamini.bsky.social, @sarahalamdari.bsky.social, and I report wet lab success rates for *over 200* different protein design tasks 🧬💻
Check out how we exploit observations of homology from evolution to design enhancers, even when we don't have prior knowledge or ability to specify function!
very very cool! i had the privilege of asking Mohammed AlQuraishi's opinion on this topic a couple years ago
Kevin K. Yang 楊凱筌
EnhancAR: Use evolution and deep learning to design enhancers with desired expression profiles!
Had a lot of fun working with Andrew Duncan, Micaela Consens @alexijie.bsky.social @lcrawford.bsky.social Jennifer Mitchell and Alan Moses!
www.biorxiv.org/content/10.6...
Learn which structural domains are compatible with each other, then generate protein sequences conditioned on the desired structural domains.
www.biorxiv.org/content/10.6...
A very nice review of AI in protein engineering from @jlistgarten.bsky.social
www.science.org/doi/10.1126/...
DISCO: a steerable, multimodal protein diffusion model that can generate enzymes for new-to-nature chemistry.
arxiv.org/abs/2604.05181
Genie3: all-atom SE3-equivariance for fast and performant protein design.
@yeqinglin.bsky.social @moalquraishi.bsky.social
www.biorxiv.org/content/10.6...
