{"schema_version":"onlylabs.public_signal.v1","title":"Google (DeepMind / Gemini) Repo: google-deepmind/alphaprotein-novo","description":"Google (DeepMind / Gemini) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/d37f9961-2e3f-42e2-940e-be4259e4fa08","json_url":"https://onlylabs.fyi/signals/d37f9961-2e3f-42e2-940e-be4259e4fa08/signal.json","generated_at":"2026-10-13T03:53:25.922Z","evidence_latest_fetched_at":"2026-10-06T07:03:38.801253+00:00","signal_first_seen_at":"2026-10-06T07:00:33.469422+00:00","org":{"slug":"google-deepmind","name":"Google (DeepMind / Gemini)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/google-deepmind","dossier_json_url":"https://onlylabs.fyi/labs/google-deepmind/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/d37f9961-2e3f-42e2-940e-be4259e4fa08","signal_json":"https://onlylabs.fyi/signals/d37f9961-2e3f-42e2-940e-be4259e4fa08/signal.json","source":"https://github.com/google-deepmind/alphaprotein-novo","lab_dossier":"https://onlylabs.fyi/labs/google-deepmind","lab_dossier_json":"https://onlylabs.fyi/labs/google-deepmind/dossier.json","analysis":"https://onlylabs.fyi/analysis/google-deepmind","analysis_json":"https://onlylabs.fyi/analysis/google-deepmind/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/google-deepmind/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":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Google (DeepMind / Gemini) published google-deepmind/alphaprotein-novo (Python). 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This package includes a diffusion model which co-generates protein structures and sequences conditioned on a catalytic motif and ligand context. This is run using `run_generator.py`. To form an end-to-end pipeline, the diffusion model is combined with optional sequence redesign using LigandMPNN (`run_ligandmpnn.py`), structure prediction using AlphaFold 3 (`run_alphafold.py`), and evaluation metrics (`evaluate_design.py`). The `run_pipeline.py` script runs all stages in sequence. See the [Quickstart](#quickstart) section for a basic launch command and [Example Design Campaigns](#example-design-campaigns) for more detailed examples. 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