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kadinaj.bsky.social

@kadinaj.bsky.social
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Reposted by @kadinaj.bsky.social
Christian Dallago @machine.learning.bio · 26/02/2026
Five years ago, we released FLIP. The core question was: can ML models for protein fitness prediction generalize in the ways that actually matter for protein engineering, i.e. low data, extrapolation to more mutations, out-of-distribution sequences?
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Reposted by @kadinaj.bsky.social
Kevin K. Yang 楊凱筌 @kevinkaichuang.bsky.social · 25/02/2026
We made FLIP2, a protein fitness benchmark spanning seven new datasets, including enzymes, protein-protein interactions, and light-sensitive proteins, as well as splits that measure generalization relevant to real-world protein engineering campaigns.
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