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protect disease outcome and outcome history.", "evidence_state": "OFFICIAL_SOURCE_VERIFIED", "sources": [ "https://arcinstitute.org/virtual-cell-initiative", "https://arcinstitute.org/news/foundation-model-stack" ] }, { "competitor_id": "COMP-CZI", "platform": "CZI Virtual Cells + CELLxGENE", "strategic_role": "Open data, model and benchmark product ecosystem", "absorb": "Entity discovery, task-based benchmarks and navigation across data and models.", "do_not_copy": "A broad model marketplace before disease tasks and adapters are stable.", "local_response": "Keep typed Disease/Gene/Cell/Dataset/Model/Study search and frozen task objects.", "evidence_state": "OFFICIAL_SOURCE_VERIFIED", "sources": [ "https://chanzuckerberg.com/newsroom/nvidia-partnership-virtual-cell-model/" ] }, { "competitor_id": "COMP-TAHOE", "platform": "Tahoe Therapeutics", "strategic_role": "Large chemical perturbation corpus and cellular foundation models", "absorb": "Dataset-to-model card, public weights and explicit licence surface.", "do_not_copy": "Cancer-cell performance as muscle or DMD transfer.", "local_response": "Run domain-shift probes; 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legacy overlap fields remain explicitly incomplete." }, { "horizon": "90_DAYS", "status": "BATCH_01_EXECUTED_PARTIAL_ACCEPTANCE", "deliverable": "Benchmark Release NMD-B1-COMPAT-HEPG2-R1 executed with five seeds, permanent-baseline results and source/adapter decisions for TxPert, STATE, Stack, Tahoe-x1 and SLIM.", "acceptance_gate": "Baseline and STATE receipts are frozen; Stack and Tahoe-x1 remain explicit hard blocks; no matched DMD task was available." }, { "horizon": "180_DAYS", "status": "AWAITING_EXPERIMENT_AND_AUTHORITY", "deliverable": "Registered DMD truth-generation Study created before execution; Prediction objects remain reserved for a separate eligible validation track.", "acceptance_gate": "Donor, intervention, dose, time, endpoint, QC, abstention and decision rule are frozen before experiment." }, { "horizon": "12_MONTHS", "status": "BLOCKED_DISEASE_TRUTH", "deliverable": "First prospective DMD perturbation benchmark with returned positive, null, toxic and failed outcomes.", "acceptance_gate": "Previously unseen donors/experiments, prespecified endpoint, stable advantage over all permanent baselines and calibrated uncertainty." } ], "evaluation_sources": [ { "source_id": "EVAL-SIMPLE-BASELINES-2025", "title": "Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines", "source_type": "PEER_REVIEWED_PRIMARY", "published_at": "2025-08-04", "url": "https://doi.org/10.1038/s41592-025-02772-6", "supports": "Permanent simple-baseline firewall.", "does_not_support": "A universal claim that every deep model must fail." }, { "source_id": "EVAL-SYSTEMA-2025", "title": "Systema: evaluating genetic perturbation response prediction beyond systematic variation", "source_type": "PEER_REVIEWED_PRIMARY", "published_at": "2025", "url": "https://www.nature.com/articles/s41587-025-02777-8", "supports": "Perturbation-specific references, matching/perturbed means and biological utility checks.", "does_not_support": "DMD validity for any evaluated model." } ], "current_identity": "TRUSTED_NMD_RESEARCH_PLATFORM_NO_DMD_PREDICTION_EVIDENCE", "upgrade_milestone": "Upgrade only after an immutable preregistered model predicts previously unseen DMD biological donors or experiments, exceeds every permanent baseline on prespecified molecular and functional endpoints across independent experiments, and returns calibration, failures and abstentions.", "claim_boundary": "External releases are monitored references. Source verification is not local execution; local execution is not DMD validation; retrospective performance is not prospective or clinical evidence." }