{ "registry_schema": "nmd-vcell-competitive-landscape/1.1", "resource_release": "v1.2.0-measured-dmd-evidence", "evidence_freeze": "2026-08-03", "interface_build": "EA-20260817-57", "checked_at": "2026-08-16", "generated_by": "scripts/apply-evidence-atlas-product-overlay.mjs", "reference_count": 14, "evidence_rule": "Competitor descriptions are design references verified against official or primary sources. Their data scale, model performance and product capability are not inherited by NMD-VCell.", "segmentation_axes": [ "competitor_type", "primary_user", "entry_task", "moat", "openness", "wet_lab_loop", "disease_specificity", "evidence_transparency", "adoption_priority" ], "records": [ { "reference_id": "LANDSCAPE-ARC-VCI", "platform": "Arc Virtual Cell Initiative · STATE · Stack · VCC", "category": "FULL_STACK_VIRTUAL_CELL", "observed_capability": "Connects large observational and perturbational atlases, open models, standardized evaluation and a held-out challenge. Stack adds in-context single-cell modeling; the 2025 challenge retained simple baselines and exposed generalization failures.", "practice_to_adopt": "Bind every prediction to a frozen task, hidden or sealed outcomes, permanent simple baselines and a ModelRun record.", "nmd_vcell_advantage": "Disease-specific evidence governance, explicit missing outcomes, candidate Study Cards and preservation of negative runs.", "current_gap": "No Arc-scale training corpus, no locally executed STATE or Stack adapter and no disease-relevant held-out perturbation outcomes.", "adoption_state": "PARTIAL_PRACTICE_ADOPTED_MODEL_NOT_INHERITED", "sources": [ { "title": "Arc Virtual Cell Initiative", "url": "https://arcinstitute.org/virtual-cell-initiative", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "Stack release", "url": "https://arcinstitute.org/news/foundation-model-stack", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "VCC 2025 wrap-up", "url": "https://arcinstitute.org/news/virtual-cell-challenge-2025-wrap-up", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "DIRECT", "primary_user": "ML developers and computational biologists", "entry_task": "Train or benchmark a perturbation model", "moat": "DATA_MODEL_BENCHMARK", "openness": "OPEN_RESEARCH", "wet_lab_loop": "PARTIAL", "disease_specificity": "GENERAL_BIOLOGY", "evidence_transparency": "HIGH", "adoption_priority": "P0", "practice_not_to_copy": "Do not present Arc-scale data, leaderboards or generalization as local capability.", "local_equivalent": [ "Data Universe", "ModelRun registry", "Benchmark dashboard" ] }, { "reference_id": "LANDSCAPE-CZI-VCP", "platform": "CZI / Biohub Virtual Cells Platform", "category": "MODEL_DATA_BENCHMARK_PLATFORM", "observed_capability": "Publishes model cards, datasets, benchmarks, CLI access and hosted workflows in one ecosystem, including TranscriptFormer and the context-specific scLDM.CD4 perturbation model.", "practice_to_adopt": "Use one adapter and metadata contract across data discovery, local execution, benchmark reporting and web presentation.", "nmd_vcell_advantage": "A narrower neuromuscular decision loop with explicit claim ceilings and source-to-experiment traceability.", "current_gap": "NMD-VCell has a run ledger but not a unified executable adapter layer or hosted inference workspace.", "adoption_state": "MODEL_RUN_LEDGER_RELEASED_ADAPTER_LAYER_NEXT", "sources": [ { "title": "Virtual Cells Platform", "url": "https://virtualcellmodels.cziscience.com/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "Virtual Cells benchmarks", "url": "https://virtualcellmodels.cziscience.com/benchmarks", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "Models catalog", "url": "https://virtualcellmodels.cziscience.com/models", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "DIRECT", "primary_user": "Biologists and ML developers", "entry_task": "Find data, select a model, run or compare", "moat": "MODEL_DATA_BENCHMARK_WORKSPACE", "openness": "OPEN_PLATFORM", "wet_lab_loop": "NO", "disease_specificity": "GENERAL_BIOLOGY", "evidence_transparency": "HIGH", "adoption_priority": "P0", "practice_not_to_copy": "Do not build a broad model marketplace before disease workflows and adapters are stable.", "local_equivalent": [ "Dataset registry", "Model cards", "ModelRun registry", "Virtual Cell Studio" ] }, { "reference_id": "LANDSCAPE-CELLXGENE", "platform": "CZ CELLxGENE Discover · Explorer · Census", "category": "SINGLE_CELL_DATA_PLATFORM", "observed_capability": "Provides versioned, ontology-harmonized single-cell data with low-latency metadata queries, source H5AD access and interoperable AnnData, Seurat and SingleCellExperiment slices.", "practice_to_adopt": "Make dataset, donor, disease, state, batch and gene selections resolve to stable source objects and exportable analysis units.", "nmd_vcell_advantage": "Neuromuscular disease interpretation and donor-aware claim ceilings rather than a general atlas browser.", "current_gap": "The current Cell Context Explorer is a typed context registry, not yet a donor-level cell browser over imported matrices.", "adoption_state": "CONTEXT_REGISTRY_RELEASED_CELL_OBJECT_IMPORT_NEXT", "sources": [ { "title": "CELLxGENE Census", "url": "https://chanzuckerberg.github.io/cellxgene-census/index", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "CELLxGENE product documentation", "url": "https://cellxgene.cziscience.com/docs/01__CellxGene", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "ADJACENT", "primary_user": "Single-cell biologists", "entry_task": "Find and explore a cell dataset", "moat": "STANDARDIZED_DATA_DISCOVERY", "openness": "OPEN_PLATFORM", "wet_lab_loop": "NO", "disease_specificity": "GENERAL_BIOLOGY", "evidence_transparency": "HIGH", "adoption_priority": "P1", "practice_not_to_copy": "Do not treat cell counts as independent biological replication.", "local_equivalent": [ "Cell Context Map", "Dataset registry" ] }, { "reference_id": "LANDSCAPE-OPEN-TARGETS", "platform": "Open Targets Platform", "category": "TARGET_DISEASE_ENTITY_GRAPH", "observed_capability": "Exposes source-provenanced target, disease, drug and association entities through a web interface, downloads, GraphQL and a versioned official MCP server.", "practice_to_adopt": "Keep stable entity identifiers, release metadata, source-level provenance and machine interfaces synchronized.", "nmd_vcell_advantage": "Cell-context, perturbation-run and prospective-study objects specialized for neuromuscular research.", "current_gap": "NMD-VCell has linked objects but lacks a general graph query or agent interface across every object type.", "adoption_state": "PARTIAL_OBJECT_GRAPH_RELEASED_QUERY_LAYER_NEXT", "sources": [ { "title": "Open Targets Platform", "url": "https://platform.opentargets.org/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "Open Targets GraphQL API", "url": "https://api.platform.opentargets.org/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "Open Targets MCP", "url": "https://mcp.platform.opentargets.org/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "ADJACENT", "primary_user": "Target and translational researchers", "entry_task": "Inspect a target–disease relationship", "moat": "ENTITY_RELATION_EVIDENCE", "openness": "OPEN_PLATFORM", "wet_lab_loop": "NO", "disease_specificity": "GENERAL_DISEASE", "evidence_transparency": "HIGH", "adoption_priority": "P0", "practice_not_to_copy": "Do not collapse heterogeneous evidence into one opaque target score.", "local_equivalent": [ "Gene records", "Disease atlas", "Evidence graph" ] }, { "reference_id": "LANDSCAPE-DEPMAP", "platform": "DepMap Portal", "category": "FUNCTIONAL_DEPENDENCY_PLATFORM", "observed_capability": "Combines recurring public CRISPR dependency releases, molecular characterization, model metadata, downloads and experimental APIs.", "practice_to_adopt": "Version data releases, model contexts and mapping files together, and expose historical release identities.", "nmd_vcell_advantage": "Disease-specific evidence boundaries prevent cancer-cell dependency from being relabeled as muscle efficacy.", "current_gap": "DepMap can add contextual dependency evidence, but it is not direct DMD muscle perturbation outcome.", "adoption_state": "REFERENCE_DATA_ONLY_NO_DMD_INHERITANCE", "sources": [ { "title": "DepMap Public 26Q1 data", "url": "https://depmap.org/portal/data_page/?tab=allData", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "DepMap APIs", "url": "https://depmap.org/portal/api/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "ADJACENT", "primary_user": "Functional genomics researchers", "entry_task": "Query dependency in a model context", "moat": "RECURRING_FUNCTIONAL_DATA", "openness": "OPEN_DATA_PORTAL", "wet_lab_loop": "PARTIAL", "disease_specificity": "CANCER", "evidence_transparency": "HIGH", "adoption_priority": "P2", "practice_not_to_copy": "Do not relabel cancer-cell dependency as neuromuscular efficacy.", "local_equivalent": [ "External context evidence", "Source buckets" ] }, { "reference_id": "LANDSCAPE-VITESSCE", "platform": "Vitessce", "category": "LINKED_SINGLE_CELL_VISUALIZATION", "observed_capability": "Uses JSON view configurations to coordinate embeddings, expression matrices, spatial images and controls over static or object-store data.", "practice_to_adopt": "Add linked gene, cell-state, donor, heatmap and spatial views after qualified AnnData-Zarr objects are registered.", "nmd_vcell_advantage": "The visual layer would inherit NMD-VCell evidence states and donor-aware statistical rules.", "current_gap": "No production AnnData-Zarr or Vitessce view configuration is registered for DMD tissue data.", "adoption_state": "VISUAL_PATTERN_VERIFIED_DATA_PREPARATION_BLOCKED", "sources": [ { "title": "Vitessce documentation", "url": "https://vitessce.io/docs/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "INFRASTRUCTURE", "primary_user": "Single-cell and spatial analysts", "entry_task": "Compose linked views over registered objects", "moat": "LINKED_VISUAL_CONFIGURATION", "openness": "OPEN_SOURCE", "wet_lab_loop": "NO", "disease_specificity": "GENERAL_BIOLOGY", "evidence_transparency": "MEDIUM", "adoption_priority": "P2", "practice_not_to_copy": "Do not add a viewer before donor-aware, checksum-addressed objects exist.", "local_equivalent": [ "Future linked cell browser" ] }, { "reference_id": "LANDSCAPE-VCELL", "platform": "VCell Modeling & Analysis Software", "category": "MECHANISTIC_SIMULATION", "observed_capability": "Separates biological model definition, applications, parameters, geometry, numerical solvers, simulation runs and downloadable results across deterministic and stochastic methods.", "practice_to_adopt": "Keep evidence checks, learned response models and future mechanistic solvers as distinct object classes with equations, parameters and run logs.", "nmd_vcell_advantage": "NMD-VCell begins from disease evidence and the experiment needed to establish a modelable transition.", "current_gap": "No calibrated mechanistic DMD model, parameter set or numerical simulation result is released.", "adoption_state": "OBJECT_SEPARATION_ADOPTED_SOLVER_LAYER_LOCKED", "sources": [ { "title": "VCell platform", "url": "https://vcell.org/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "VCell source repository", "url": "https://github.com/virtualcell/vcell", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "ADJACENT", "primary_user": "Systems and computational biologists", "entry_task": "Build and simulate a mechanistic model", "moat": "MECHANISTIC_SIMULATION", "openness": "OPEN_SOURCE", "wet_lab_loop": "NO", "disease_specificity": "GENERAL_BIOLOGY", "evidence_transparency": "HIGH", "adoption_priority": "P2", "practice_not_to_copy": "Do not relabel an evidence graph as a calibrated mechanistic simulation.", "local_equivalent": [ "DMD process map", "Future mechanistic solver lane" ] }, { "reference_id": "LANDSCAPE-TAHOE", "platform": "Tahoe · Mosaic · Tahoe-100M", "category": "PERTURBATION_DATA_ENGINE", "observed_capability": "Builds large perturbational single-cell maps and publishes Tahoe-100M as a reusable data product with manuscript, download, community and model entry points.", "practice_to_adopt": "Treat every important disease dataset as a versioned product with a scientific question, access path, limitations, examples and downstream model links.", "nmd_vcell_advantage": "NMD-VCell can make donor structure, neuromuscular context and claim ceilings more visible than a scale-first perturbation atlas.", "current_gap": "No flagship NMD perturbation dataset currently connects a public Dataset Card to measured outcomes, model runs and follow-up studies.", "adoption_state": "DATA_PRODUCT_PATTERN_ADOPT_NOW_SCALE_NOT_INHERITED", "sources": [ { "title": "Tahoe platform", "url": "https://www.tahoebio.ai/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "Open sourcing Tahoe-100M", "url": "https://www.tahoebio.ai/news/open-sourcing-tahoe-100m", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "DIRECT", "primary_user": "Virtual-cell model builders and drug discovery teams", "entry_task": "Access a large chemical perturbation atlas", "moat": "PROPRIETARY_DATA_ENGINE_WITH_OPEN_RELEASES", "openness": "MIXED", "wet_lab_loop": "YES", "disease_specificity": "CANCER_FIRST", "evidence_transparency": "MEDIUM", "adoption_priority": "P1", "practice_not_to_copy": "Do not use cell-count scale as a substitute for donor, disease and perturbation relevance.", "local_equivalent": [ "Measured DMD evidence release", "Dataset cards" ] }, { "reference_id": "LANDSCAPE-XAIRA-XCELL", "platform": "Xaira Therapeutics · X-Cell", "category": "CAUSAL_PERTURBATION_MODEL", "observed_capability": "Packages a virtual-cell release around a named model, a large perturbation training asset, a technical report and a cross-context prediction task.", "practice_to_adopt": "Give each locally evaluated model a permanent release page that binds model version, training context, target context, artifacts, benchmark results and limitations.", "nmd_vcell_advantage": "NMD-VCell publishes negative runs, abstentions and disease-specific transfer boundaries instead of relying on a broad model launch claim.", "current_gap": "Model releases are audited, but the public product does not yet present every ModelRun as one linked release package with quickstart and failure analysis.", "adoption_state": "MODEL_RELEASE_CONTRACT_ADOPT_PERFORMANCE_NOT_INHERITED", "sources": [ { "title": "X-Cell announcement", "url": "https://www.xaira.com/news/announcing-x-cell-our-virtual-cell-model-trained-on-billions-of-genomic-data-points", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "X-Cell technical report", "url": "https://www.cdn.xaira.com/papers/X_CELL_V1_0316_final.pdf", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "DIRECT", "primary_user": "AI drug discovery researchers", "entry_task": "Evaluate or apply a cross-context perturbation model", "moat": "PROPRIETARY_PERTURBATION_DATA_AND_MODEL", "openness": "MIXED", "wet_lab_loop": "YES", "disease_specificity": "GENERAL_CELL_CONTEXTS", "evidence_transparency": "MEDIUM", "adoption_priority": "P1", "practice_not_to_copy": "Do not claim cross-context DMD generalization without prospective disease-relevant validation.", "local_equivalent": [ "Model card audit", "ModelRun release objects" ] }, { "reference_id": "LANDSCAPE-RECURSION-OS", "platform": "Recursion OS · Predict–Explain–Discover", "category": "LAB_IN_THE_LOOP_DRUG_DISCOVERY", "observed_capability": "Connects automated perturbation experiments, phenomics and transcriptomics, learned maps, design workflows and downstream therapeutic programs in a physical-to-digital feedback loop.", "practice_to_adopt": "Show the complete lifecycle from experimental material through assay, data object, model, decision, new experiment and evidence update.", "nmd_vcell_advantage": "NMD-VCell can expose each evidence transition and missing link publicly even without proprietary lab scale or a drug pipeline.", "current_gap": "Study Cards and an outcome registry exist, but no measured candidate-level DMD outcome has yet completed the loop.", "adoption_state": "CLOSED_LOOP_SCHEMA_ADOPTED_MEASURED_RETURN_PENDING", "sources": [ { "title": "Recursion OS", "url": "https://www.recursion.com/platform", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "Virtual Cells: Predict, Explain, Discover", "url": "https://arxiv.org/abs/2505.14613", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "DIRECT", "primary_user": "Drug discovery programs", "entry_task": "Move from perturbation maps to a therapeutic program", "moat": "AUTOMATED_LAB_DATA_MODEL_LOOP", "openness": "PROPRIETARY", "wet_lab_loop": "YES", "disease_specificity": "THERAPEUTIC_PROGRAMS", "evidence_transparency": "MEDIUM", "adoption_priority": "P0", "practice_not_to_copy": "Do not imply an automated wet-lab or therapeutic pipeline that NMD-VCell does not operate.", "local_equivalent": [ "Evidence-to-experiment loop", "Study Cards", "Outcome registry" ] }, { "reference_id": "LANDSCAPE-GENBIO-AIDO", "platform": "GenBio AI · AIDO", "category": "MULTISCALE_WORLD_MODEL", "observed_capability": "Frames DNA, RNA, protein, structure and single-cell models as interoperable modules on a roadmap toward a multiscale biological world model.", "practice_to_adopt": "Publish a layered capability roadmap that distinguishes released modules, interfaces between scales and future simulation goals.", "nmd_vcell_advantage": "NMD-VCell can make every currently supported scale and unsupported transition explicit rather than presenting a universal world-model claim.", "current_gap": "Gene, pathway, cell-state and experiment objects are linked conceptually but do not yet share one typed cross-scale relation contract.", "adoption_state": "ROADMAP_PATTERN_ADOPT_UNIVERSAL_CLAIM_REJECTED", "sources": [ { "title": "GenBio AI AIDO", "url": "https://genbio.ai/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" }, { "title": "A World Model of the Virtual Cell", "url": "https://genbio.ai/world-model-of-the-virtual-cell/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "DIRECT", "primary_user": "Multiscale biological AI researchers", "entry_task": "Use or combine biological foundation models", "moat": "MULTISCALE_MODEL_SYSTEM", "openness": "MIXED", "wet_lab_loop": "NO", "disease_specificity": "GENERAL_BIOLOGY", "evidence_transparency": "LOW_TO_MEDIUM", "adoption_priority": "P2", "practice_not_to_copy": "Do not use world-model language where only bounded evidence objects are available.", "local_equivalent": [ "Meaning map", "Capability atlas" ] }, { "reference_id": "LANDSCAPE-CELLULAR-INTELLIGENCE", "platform": "Cellular Intelligence", "category": "TEMPORAL_SIGNALING_ACTIVE_LEARNING", "observed_capability": "Emphasizes sequential signaling, dose, temporal order, cell-fate control and active learning over static cell-state representation.", "practice_to_adopt": "Represent intervention sequence, dose and time as first-class variables, then prioritize experiments by the uncertainty they can resolve.", "nmd_vcell_advantage": "The DMD process map and Study Cards can connect temporal signaling hypotheses to source-linked disease evidence and explicit outcome branches.", "current_gap": "Current studies record time and endpoint, but the planner does not yet compare sequential interventions or information gain across an experiment portfolio.", "adoption_state": "TEMPORAL_CONTRACT_NEXT_ACTIVE_LEARNING_FUTURE", "sources": [ { "title": "Cellular Intelligence virtual cell-signaling model", "url": "https://www.cellularintelligence.com/news/somite-becomes-cellular-intelligence", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "DIRECT", "primary_user": "Regenerative medicine and cell engineering teams", "entry_task": "Design temporal signaling interventions", "moat": "SEQUENTIAL_SIGNALING_DATA_ENGINE", "openness": "PROPRIETARY", "wet_lab_loop": "YES", "disease_specificity": "CELL_FATE", "evidence_transparency": "LOW_TO_MEDIUM", "adoption_priority": "P1", "practice_not_to_copy": "Do not inherit company-reported scale or efficiency claims as validated evidence.", "local_equivalent": [ "DMD process map", "Experiment Planner" ] }, { "reference_id": "LANDSCAPE-CELLARIUM", "platform": "Broad Cellarium AI", "category": "SINGLE_CELL_DATAOPS_MLOPS", "observed_capability": "Presents annotation, denoising, perturbation interpretation and cloud infrastructure as separate tools with task-specific identities.", "practice_to_adopt": "Turn existing NMD-VCell pages into a tool catalog only when each module has declared inputs, outputs, failure states, examples and machine interfaces.", "nmd_vcell_advantage": "NMD-VCell can bind each tool output to disease evidence, a claim ceiling and the next experiment instead of ending at a generic analysis artifact.", "current_gap": "Resolver, comparator, process inspector, model auditor and planner exist but do not yet share a visible tool contract.", "adoption_state": "TOOL_CATALOG_CONTRACT_IMPLEMENT_THIS_BUILD", "sources": [ { "title": "Cellarium AI tools", "url": "https://www.cellarium.ai/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "INFRASTRUCTURE", "primary_user": "Single-cell computational teams", "entry_task": "Run a task-specific single-cell tool", "moat": "DATAOPS_MLOPS_AND_TOOLING", "openness": "OPEN_RESEARCH", "wet_lab_loop": "NO", "disease_specificity": "GENERAL_BIOLOGY", "evidence_transparency": "HIGH", "adoption_priority": "P0", "practice_not_to_copy": "Do not label an informational page as a tool without executable inputs and outputs.", "local_equivalent": [ "Resolver", "Compare", "Process inspector", "Model auditor", "Planner" ] }, { "reference_id": "LANDSCAPE-NVIDIA-BIONEMO", "platform": "NVIDIA BioNeMo", "category": "MODEL_DEPLOYMENT_INFRASTRUCTURE", "observed_capability": "Packages biomolecular AI capabilities as frameworks, web interfaces, APIs and deployable inference microservices.", "practice_to_adopt": "Keep one model identity across web documentation, API records, local adapters, artifacts and deployment-specific run receipts.", "nmd_vcell_advantage": "NMD-VCell can provide stronger disease-specific evidence governance around externally executed or locally adapted models.", "current_gap": "The public API exposes ModelRuns, but there is no uniform adapter package or deployment receipt across external model families.", "adoption_state": "PACKAGING_CONTRACT_NEXT_INFRASTRUCTURE_NOT_REQUIRED", "sources": [ { "title": "NVIDIA BioNeMo", "url": "https://www.nvidia.com/en-us/clara/bionemo/", "source_class": "OFFICIAL_OR_PRIMARY_SOURCE", "checked_at": "2026-08-16" } ], "competitor_type": "INFRASTRUCTURE", "primary_user": "AI developers and enterprise research teams", "entry_task": "Build, adapt or deploy a biology model", "moat": "COMPUTE_PACKAGING_AND_DEPLOYMENT", "openness": "MIXED", "wet_lab_loop": "NO", "disease_specificity": "DRUG_DISCOVERY", "evidence_transparency": "MEDIUM", "adoption_priority": "P1", "practice_not_to_copy": "Do not build enterprise infrastructure before a reproducible disease adapter is needed.", "local_equivalent": [ "API", "Model adapters", "Run receipts" ] } ], "nmd_vcell_position": "Compete on neuromuscular disease specificity, source-to-decision traceability, baseline-first model audits and prospective evidence governance—not on unsupported scale parity.", "claim_boundary": "This registry is not a universal product ranking and does not imply parity, endorsement or local model validation." }