{"schema_version":"onlylabs.public_signal.v1","title":"Microsoft Model: microsoft/Dayhoff-170M-UR90-HL-44000","description":"Microsoft model signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/79be6b27-034f-4d41-8be6-721611214850","json_url":"https://onlylabs.fyi/signals/79be6b27-034f-4d41-8be6-721611214850/signal.json","generated_at":"2026-06-26T23:24:08.135Z","evidence_latest_fetched_at":"2026-06-26T07:02:42.729312+00:00","signal_first_seen_at":"2026-06-26T07:00:30.529403+00:00","org":{"slug":"microsoft","name":"Microsoft","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/microsoft","dossier_json_url":"https://onlylabs.fyi/labs/microsoft/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/79be6b27-034f-4d41-8be6-721611214850","signal_json":"https://onlylabs.fyi/signals/79be6b27-034f-4d41-8be6-721611214850/signal.json","source":"https://huggingface.co/microsoft/Dayhoff-170M-UR90-HL-44000","lab_dossier":"https://onlylabs.fyi/labs/microsoft","lab_dossier_json":"https://onlylabs.fyi/labs/microsoft/dossier.json","analysis":"https://onlylabs.fyi/analysis/microsoft","analysis_json":"https://onlylabs.fyi/analysis/microsoft/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/microsoft/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml","data_business":null},"answer_pack":{"answer":"Microsoft published microsoft/Dayhoff-170M-UR90-HL-44000. 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These models can natively predict zero-shot mutation effects on fitness, scaffold structural motifs by conditioning on evolutionary or structural context, and perform guided generation of novel proteins within specified families. Learning from metagenomic and structure-based synthetic data from the Dayhoff Atlas increased the cellular expression rates of generated proteins, highlighting the real-world value of expanding the scale, diversity, and novelty of protein sequence data. The Dayhoff architecture is a hybrid of state-space Mamba layers and Transformer self-attention, interleaved with Mixture-of-Experts modules to maximize capacity while preserving efficiency. It natively handles long contexts, allowing both single sequences and unrolled MSAs to be modeled. 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