{
  "version": "1.0.0",
  "generated": "2026-05-12",
  "count": 79,
  "groups": [
    {
      "id": "broad-institute",
      "name": "Broad Institute",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://www.broadinstitute.org/",
      "github": "https://github.com/broadinstitute",
      "primary_benchmarks": [
        "depmap",
        "lincs-l1000"
      ],
      "expert_ids": [
        "todd-golub",
        "aviad-tsherniak",
        "william-hahn",
        "aravind-subramanian",
        "christina-theodoris"
      ],
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        "quality_median": 5,
        "breadth": 5,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "DepMap + CMap anchor much of cancer ML.",
      "initiatives_hosted": [],
      "composite_score": 100.0
    },
    {
      "id": "oxford-opig",
      "name": "Oxford OPIG (Deane Lab)",
      "kind": "lab",
      "type": "academic",
      "country": "UK",
      "website": "https://opig.stats.ox.ac.uk/",
      "github": "https://github.com/oxpig",
      "primary_benchmarks": [
        "posebusters",
        "sabdab",
        "oas",
        "cov-abdab"
      ],
      "expert_ids": [
        "charlotte-deane",
        "martin-buttenschoen",
        "tobias-olsen",
        "matthew-raybould",
        "james-dunbar"
      ],
      "rubric": {
        "output_volume": 5,
        "quality_median": 5,
        "breadth": 5,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Premier antibody + structure benchmarking lab.",
      "initiatives_hosted": [
        "posebusters-initiative"
      ],
      "composite_score": 100.0
    },
    {
      "id": "embl-ebi",
      "name": "EMBL-EBI",
      "kind": "consortium",
      "type": "academic",
      "country": "UK",
      "website": "https://www.ebi.ac.uk/",
      "github": "https://github.com/EBIvariation",
      "primary_benchmarks": [
        "chembl",
        "open-targets",
        "stringdb"
      ],
      "expert_ids": [
        "andrew-leach",
        "barbara-zdrazil",
        "ian-dunham",
        "ellen-mcdonagh"
      ],
      "rubric": {
        "output_volume": 4,
        "quality_median": 5,
        "breadth": 5,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Pillar of European bioinformatics infrastructure.",
      "initiatives_hosted": [
        "elixir"
      ],
      "composite_score": 97.4
    },
    {
      "id": "mit-csail",
      "name": "MIT CSAIL / Jameel Clinic / Coley Lab",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://www.csail.mit.edu/",
      "github": "https://github.com/mit-csail",
      "primary_benchmarks": [
        "admet-ai",
        "pmo",
        "uspto-retrosyn"
      ],
      "expert_ids": [
        "regina-barzilay",
        "connor-coley",
        "wenhao-gao"
      ],
      "rubric": {
        "output_volume": 4,
        "quality_median": 5,
        "breadth": 5,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Jameel Clinic antibiotic discovery + Coley retrosynthesis.",
      "initiatives_hosted": [],
      "composite_score": 97.4
    },
    {
      "id": "zitnik-lab",
      "name": "Zitnik Lab",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://zitniklab.hms.harvard.edu/",
      "github": "https://github.com/mims-harvard",
      "primary_benchmarks": [
        "tdc-admet",
        "primekg",
        "hint-trialbench",
        "herg-classifier-bench",
        "ames",
        "dili-ldi",
        "tdc-drug-syn"
      ],
      "expert_ids": [
        "marinka-zitnik",
        "kexin-huang",
        "tianfan-fu",
        "payal-chandak"
      ],
      "rubric": {
        "output_volume": 5,
        "quality_median": 5,
        "breadth": 5,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Drives TDC, PrimeKG, and trial outcome benchmarks. Among the highest-output academic benchmark labs.",
      "initiatives_hosted": [
        "tdc"
      ],
      "composite_score": 96.9
    },
    {
      "id": "broad-depmap",
      "name": "Broad DepMap",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://depmap.org/",
      "github": "https://github.com/broadinstitute/depmap",
      "primary_benchmarks": [
        "depmap"
      ],
      "expert_ids": [
        "aviad-tsherniak",
        "william-hahn",
        "todd-golub"
      ],
      "rubric": {
        "output_volume": 4,
        "quality_median": 5,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Project Achilles + DepMap quarterly releases.",
      "initiatives_hosted": [],
      "composite_score": 95.0
    },
    {
      "id": "biozentrum-basel",
      "name": "Biozentrum Basel / SIB",
      "kind": "lab",
      "type": "academic",
      "country": "Switzerland",
      "website": "https://schwedelab.org/",
      "github": "https://github.com/plinder-org",
      "primary_benchmarks": [
        "plinder",
        "pinder",
        "cameo-targets"
      ],
      "expert_ids": [
        "torsten-schwede"
      ],
      "rubric": {
        "output_volume": 4,
        "quality_median": 5,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "CAMEO + PLINDER / PINDER leadership.",
      "initiatives_hosted": [
        "cameo",
        "plinder-initiative"
      ],
      "composite_score": 95.0
    },
    {
      "id": "sanger",
      "name": "Wellcome Sanger Institute",
      "kind": "lab",
      "type": "academic",
      "country": "UK",
      "website": "https://www.sanger.ac.uk/",
      "github": "https://github.com/wtsi-hgi",
      "primary_benchmarks": [
        "open-targets"
      ],
      "expert_ids": [],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 5,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Genomics + Open Targets partner.",
      "initiatives_hosted": [
        "elixir"
      ],
      "composite_score": 94.8
    },
    {
      "id": "sib-swiss",
      "name": "SIB Swiss Institute of Bioinformatics",
      "kind": "consortium",
      "type": "academic",
      "country": "Switzerland",
      "website": "https://www.sib.swiss/",
      "github": "https://github.com/SIB-swiss",
      "primary_benchmarks": [
        "stringdb",
        "cameo-targets"
      ],
      "expert_ids": [
        "christian-von-mering",
        "torsten-schwede"
      ],
      "rubric": {
        "output_volume": 4,
        "quality_median": 5,
        "breadth": 5,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "STRING + SWISS-MODEL + CAMEO.",
      "initiatives_hosted": [
        "elixir",
        "cameo"
      ],
      "composite_score": 94.0
    },
    {
      "id": "insilico-medicine",
      "name": "Insilico Medicine",
      "kind": "company",
      "type": "industry",
      "country": "UAE / USA / China",
      "website": "https://insilico.com/",
      "github": "N/A",
      "primary_benchmarks": [
        "moses",
        "longevity-bench-insilico",
        "targetbench-insilico",
        "clinbench-quarterly",
        "pli-gpcr-suite",
        "ism-admet",
        "polaris-biologics"
      ],
      "expert_ids": [
        "alex-zhavoronkov",
        "alex-aliper",
        "alex-zhebrak",
        "daniil-polykovskiy"
      ],
      "rubric": {
        "output_volume": 5,
        "quality_median": 4,
        "breadth": 5,
        "openness": 4,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Clinical-stage AI drug discovery company with INS018_055 in Phase 2 (IPF) and 11+ programs. Released ScienceAIBench / InsilicoBench / DDB with 200+ live benchmarks each \u2014 covering a unique longevity slice that no other org provides. Frontier-LLM leaderboards are externally benchmarked (not self-referential). Translational signal: clinical-stage pipeline. #1 in aging/longevity biotech.",
      "initiatives_hosted": [
        "insilico-scienceaibench",
        "insilico-insilicobench",
        "insilico-ddb"
      ],
      "composite_score": 93.8
    },
    {
      "id": "tdc",
      "name": "Therapeutics Data Commons",
      "kind": "consortium",
      "type": "academic",
      "country": "USA",
      "website": "https://tdcommons.ai/",
      "github": "https://github.com/mims-harvard/TDC",
      "primary_benchmarks": [
        "tdc-admet",
        "herg-classifier-bench",
        "ames",
        "dili-ldi",
        "tdc-drug-syn",
        "uspto-retrosyn",
        "moleculenet",
        "guacamol",
        "moses",
        "pmo",
        "dude",
        "disgenet",
        "primekg",
        "tox21",
        "toxcast",
        "clintox",
        "offsides-twosides",
        "pkpd-obach",
        "pli-gpcr-suite",
        "hint-trialbench"
      ],
      "expert_ids": [
        "marinka-zitnik",
        "kexin-huang",
        "tianfan-fu"
      ],
      "rubric": {
        "output_volume": 5,
        "quality_median": 5,
        "breadth": 5,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Broadest ML-ready benchmark hub.",
      "initiatives_hosted": [
        "tdc"
      ],
      "composite_score": 93.6
    },
    {
      "id": "opentargets",
      "name": "Open Targets",
      "kind": "consortium",
      "type": "academic",
      "country": "UK",
      "website": "https://www.opentargets.org/",
      "github": "https://github.com/opentargets",
      "primary_benchmarks": [
        "open-targets"
      ],
      "expert_ids": [
        "ian-dunham",
        "ellen-mcdonagh"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "GSK + Sanofi + Bayer + MSK + EBI + Sanger target prioritization platform.",
      "initiatives_hosted": [
        "elixir"
      ],
      "composite_score": 92.4
    },
    {
      "id": "mit-lcp",
      "name": "MIT Lab for Computational Physiology",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://lcp.mit.edu/",
      "github": "https://github.com/MIT-LCP",
      "primary_benchmarks": [
        "mimic-benchmark"
      ],
      "expert_ids": [
        "leo-anthony-celi",
        "alistair-johnson",
        "roger-mark"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "MIMIC. Clinical ML canonical host.",
      "initiatives_hosted": [
        "mimic"
      ],
      "composite_score": 92.4
    },
    {
      "id": "coley-lab",
      "name": "Coley Lab (MIT)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://coley.mit.edu/",
      "github": "https://github.com/coleygroup",
      "primary_benchmarks": [
        "pmo",
        "uspto-retrosyn",
        "ord-bench"
      ],
      "expert_ids": [
        "connor-coley",
        "wenhao-gao"
      ],
      "rubric": {
        "output_volume": 4,
        "quality_median": 5,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Synthesis ML anchor.",
      "initiatives_hosted": [
        "open-reaction-database"
      ],
      "composite_score": 91.7
    },
    {
      "id": "prediction-center-ucd",
      "name": "CASP Prediction Center",
      "kind": "consortium",
      "type": "academic",
      "country": "USA",
      "website": "https://predictioncenter.org/",
      "github": "N/A",
      "primary_benchmarks": [
        "casp15",
        "casp16"
      ],
      "expert_ids": [
        "andriy-kryshtafovych",
        "john-moult"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "CASP host.",
      "initiatives_hosted": [
        "casp"
      ],
      "composite_score": 90.0
    },
    {
      "id": "dfci",
      "name": "Dana-Farber Cancer Institute",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://www.dana-farber.org/",
      "github": "N/A",
      "primary_benchmarks": [
        "scperturb",
        "depmap"
      ],
      "expert_ids": [
        "chris-sander",
        "william-hahn"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Cancer-focused computational biology anchor.",
      "initiatives_hosted": [],
      "composite_score": 89.3
    },
    {
      "id": "recursion",
      "name": "Recursion Pharmaceuticals",
      "kind": "company",
      "type": "industry",
      "country": "USA",
      "website": "https://www.recursion.com/",
      "github": "https://github.com/recursionpharma",
      "primary_benchmarks": [
        "polaris-admet"
      ],
      "expert_ids": [
        "jonathan-hsu"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 4,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Phenomics benchmarks (RxRx) + Polaris partner.",
      "initiatives_hosted": [
        "polaris"
      ],
      "composite_score": 88.8
    },
    {
      "id": "helmholtz-munich",
      "name": "Helmholtz Munich (Theis Lab)",
      "kind": "lab",
      "type": "academic",
      "country": "Germany",
      "website": "https://www.helmholtz-munich.de/icb/",
      "github": "https://github.com/theislab",
      "primary_benchmarks": [
        "scperturb",
        "openproblems-perturbation"
      ],
      "expert_ids": [
        "fabian-theis",
        "malte-luecken"
      ],
      "rubric": {
        "output_volume": 4,
        "quality_median": 5,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Single-cell foundation model ecosystem.",
      "initiatives_hosted": [],
      "composite_score": 88.6
    },
    {
      "id": "marks-lab",
      "name": "Marks Lab (Debora Marks)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://www.deboramarkslab.com/",
      "github": "https://github.com/OATML-Markslab",
      "primary_benchmarks": [
        "proteingym"
      ],
      "expert_ids": [
        "debora-marks",
        "pascal-notin"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "ProteinGym + EVE + EVcouplings.",
      "initiatives_hosted": [
        "proteingym"
      ],
      "composite_score": 86.9
    },
    {
      "id": "tatonetti-lab",
      "name": "Tatonetti Lab (Cedars-Sinai)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://tatonettilab.org/",
      "github": "https://github.com/tatonetti-lab",
      "primary_benchmarks": [
        "offsides-twosides"
      ],
      "expert_ids": [
        "nick-tatonetti"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 4,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Pharmacovigilance ML.",
      "initiatives_hosted": [
        "faers"
      ],
      "composite_score": 86.4
    },
    {
      "id": "genentech-gred",
      "name": "Genentech gRED (ML)",
      "kind": "lab",
      "type": "industry",
      "country": "USA",
      "website": "https://www.gene.com/scientists/",
      "github": "https://github.com/genentech",
      "primary_benchmarks": [
        "perturbbench"
      ],
      "expert_ids": [
        "aviv-regev"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 4,
        "breadth": 4,
        "openness": 4,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Large-pharma ML research org.",
      "initiatives_hosted": [],
      "composite_score": 86.2
    },
    {
      "id": "sander-lab",
      "name": "Sander Lab (Harvard/DFCI)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "http://sanderlab.org/",
      "github": "https://github.com/sanderlab",
      "primary_benchmarks": [
        "scperturb"
      ],
      "expert_ids": [
        "chris-sander"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "cBio + single-cell perturbation leadership.",
      "initiatives_hosted": [],
      "composite_score": 86.0
    },
    {
      "id": "microsoft-research",
      "name": "Microsoft Research AI4Science",
      "kind": "lab",
      "type": "industry",
      "country": "International",
      "website": "https://www.microsoft.com/en-us/research/lab/microsoft-research-ai4science/",
      "github": "https://github.com/microsoft",
      "primary_benchmarks": [
        "guacamol",
        "moses"
      ],
      "expert_ids": [
        "marwin-segler",
        "daniil-polykovskiy"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 4,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "MoLeR / Polygon / Chemprop contributions.",
      "initiatives_hosted": [],
      "composite_score": 85.5
    },
    {
      "id": "barzilay-lab",
      "name": "Barzilay Group (MIT)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://people.csail.mit.edu/regina/",
      "github": "https://github.com/swansonk14",
      "primary_benchmarks": [
        "admet-ai"
      ],
      "expert_ids": [
        "regina-barzilay",
        "kyle-swanson"
      ],
      "rubric": {
        "output_volume": 2,
        "quality_median": 5,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 4,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "ADMET-AI + halicin.",
      "initiatives_hosted": [],
      "composite_score": 85.0
    },
    {
      "id": "valence-labs",
      "name": "Valence Labs (Recursion)",
      "kind": "company",
      "type": "industry",
      "country": "Canada",
      "website": "https://www.valencelabs.com/",
      "github": "https://github.com/valence-labs",
      "primary_benchmarks": [
        "polaris-admet",
        "polaris-biologics"
      ],
      "expert_ids": [
        "cas-wognum",
        "emmanuel-noutahi"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 4,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Co-runs Polaris; Datamol ecosystem.",
      "initiatives_hosted": [
        "polaris"
      ],
      "composite_score": 84.3
    },
    {
      "id": "broad-cmap",
      "name": "Broad CMap",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://clue.io/",
      "github": "https://github.com/cmap",
      "primary_benchmarks": [
        "lincs-l1000"
      ],
      "expert_ids": [
        "aravind-subramanian",
        "todd-golub"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 4,
        "breadth": 3,
        "openness": 4,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Connectivity Map + LINCS L1000.",
      "initiatives_hosted": [],
      "composite_score": 83.8
    },
    {
      "id": "cz-biohub",
      "name": "CZ Biohub San Francisco",
      "kind": "lab",
      "type": "nonprofit",
      "country": "USA",
      "website": "https://www.czbiohub.org/",
      "github": "https://github.com/czbiohub-sf",
      "primary_benchmarks": [
        "cz-virtual-cell-challenge"
      ],
      "expert_ids": [
        "stephen-quake",
        "ambrose-carr"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
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        "openness": 5,
        "industry_uptake": 4,
        "longevity": 4,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Virtual Cell Challenge host.",
      "initiatives_hosted": [
        "czi-virtual-cell"
      ],
      "composite_score": 83.6
    },
    {
      "id": "materials-project",
      "name": "Materials Project / LBL",
      "kind": "lab",
      "type": "government",
      "country": "USA",
      "website": "https://materialsproject.org/",
      "github": "https://github.com/materialsproject",
      "primary_benchmarks": [
        "matbench"
      ],
      "expert_ids": [
        "anubhav-jain"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "MatBench + Materials Project.",
      "initiatives_hosted": [],
      "composite_score": 83.3
    },
    {
      "id": "nih-ncbi",
      "name": "NIH NCBI (PubChem)",
      "kind": "lab",
      "type": "government",
      "country": "USA",
      "website": "https://www.ncbi.nlm.nih.gov/",
      "github": "N/A",
      "primary_benchmarks": [
        "pubchem-bioassay"
      ],
      "expert_ids": [
        "sunghwan-kim"
      ],
      "rubric": {
        "output_volume": 2,
        "quality_median": 5,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "PubChem + BioAssay.",
      "initiatives_hosted": [],
      "composite_score": 83.3
    },
    {
      "id": "polaris",
      "name": "Polaris Consortium",
      "kind": "consortium",
      "type": "industry",
      "country": "International",
      "website": "https://polarishub.io/",
      "github": "https://github.com/polaris-hub",
      "primary_benchmarks": [
        "polaris-admet",
        "polaris-biologics"
      ],
      "expert_ids": [
        "cas-wognum",
        "emmanuel-noutahi",
        "jonathan-hsu"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 3,
        "openness": 4,
        "industry_uptake": 5,
        "longevity": 3,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Industry benchmark governance body.",
      "initiatives_hosted": [
        "polaris"
      ],
      "composite_score": 82.6
    },
    {
      "id": "sage-bionetworks",
      "name": "Sage Bionetworks (DREAM)",
      "kind": "consortium",
      "type": "nonprofit",
      "country": "USA",
      "website": "https://sagebionetworks.org/",
      "github": "https://github.com/Sage-Bionetworks",
      "primary_benchmarks": [],
      "expert_ids": [
        "gustavo-stolovitzky",
        "justin-guinney"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 4,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "DREAM Challenges host.",
      "initiatives_hosted": [
        "dream"
      ],
      "composite_score": 82.4
    },
    {
      "id": "novartis-nibr",
      "name": "Novartis BR / AIDD",
      "kind": "company",
      "type": "industry",
      "country": "Switzerland",
      "website": "https://www.novartis.com/research-development",
      "github": "N/A",
      "primary_benchmarks": [
        "polaris-admet"
      ],
      "expert_ids": [],
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        "output_volume": 2,
        "quality_median": 5,
        "breadth": 3,
        "openness": 3,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Contributes Polaris ADMET endpoints.",
      "initiatives_hosted": [
        "polaris"
      ],
      "composite_score": 82.1
    },
    {
      "id": "gsk",
      "name": "GSK Computational Biology",
      "kind": "company",
      "type": "industry",
      "country": "UK",
      "website": "https://www.gsk.com/",
      "github": "N/A",
      "primary_benchmarks": [
        "open-targets"
      ],
      "expert_ids": [],
      "rubric": {
        "output_volume": 2,
        "quality_median": 5,
        "breadth": 3,
        "openness": 3,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Open Targets co-funder.",
      "initiatives_hosted": [],
      "composite_score": 82.1
    },
    {
      "id": "icr-london",
      "name": "The Institute of Cancer Research",
      "kind": "lab",
      "type": "academic",
      "country": "UK",
      "website": "https://www.icr.ac.uk/",
      "github": "N/A",
      "primary_benchmarks": [
        "cansar"
      ],
      "expert_ids": [
        "bissan-al-lazikani"
      ],
      "rubric": {
        "output_volume": 2,
        "quality_median": 5,
        "breadth": 3,
        "openness": 4,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "canSAR host.",
      "initiatives_hosted": [],
      "composite_score": 81.7
    },
    {
      "id": "oatml",
      "name": "Oxford OATML",
      "kind": "lab",
      "type": "academic",
      "country": "UK",
      "website": "https://oatml.cs.ox.ac.uk/",
      "github": "https://github.com/OATML",
      "primary_benchmarks": [
        "proteingym"
      ],
      "expert_ids": [
        "yarin-gal",
        "pascal-notin"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 4,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "ML research + ProteinGym engineering.",
      "initiatives_hosted": [
        "proteingym"
      ],
      "composite_score": 81.2
    },
    {
      "id": "astrazeneca",
      "name": "AstraZeneca AIDD",
      "kind": "company",
      "type": "industry",
      "country": "UK/Sweden",
      "website": "https://www.astrazeneca.com/r-d.html",
      "github": "https://github.com/AstraZeneca",
      "primary_benchmarks": [
        "polaris-admet"
      ],
      "expert_ids": [],
      "rubric": {
        "output_volume": 3,
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        "breadth": 3,
        "openness": 3,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Contributes Polaris benchmarks + AZ internal CAS work.",
      "initiatives_hosted": [
        "polaris"
      ],
      "composite_score": 81.2
    },
    {
      "id": "fda-cder",
      "name": "FDA CDER",
      "kind": "lab",
      "type": "government",
      "country": "USA",
      "website": "https://www.fda.gov/drugs/",
      "github": "N/A",
      "primary_benchmarks": [
        "faers-bench"
      ],
      "expert_ids": [],
      "rubric": {
        "output_volume": 2,
        "quality_median": 4,
        "breadth": 3,
        "openness": 4,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "FAERS + regulatory science.",
      "initiatives_hosted": [
        "faers"
      ],
      "composite_score": 81.2
    },
    {
      "id": "google-research",
      "name": "Google Research (Kearnes)",
      "kind": "lab",
      "type": "industry",
      "country": "USA",
      "website": "https://research.google/",
      "github": "https://github.com/google-research",
      "primary_benchmarks": [
        "ord-bench"
      ],
      "expert_ids": [
        "steven-kearnes"
      ],
      "rubric": {
        "output_volume": 2,
        "quality_median": 5,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "ORD co-foundation.",
      "initiatives_hosted": [
        "open-reaction-database"
      ],
      "composite_score": 81.0
    },
    {
      "id": "ncats",
      "name": "NIH NCATS",
      "kind": "lab",
      "type": "government",
      "country": "USA",
      "website": "https://ncats.nih.gov/",
      "github": "N/A",
      "primary_benchmarks": [
        "tox21"
      ],
      "expert_ids": [
        "ruili-huang"
      ],
      "rubric": {
        "output_volume": 2,
        "quality_median": 4,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Tox21 + translational informatics.",
      "initiatives_hosted": [],
      "composite_score": 80.7
    },
    {
      "id": "rostlab-tum",
      "name": "Rostlab (TU M\u00fcnchen)",
      "kind": "lab",
      "type": "academic",
      "country": "Germany",
      "website": "https://www.rostlab.org/",
      "github": "https://github.com/Rostlab",
      "primary_benchmarks": [
        "flip"
      ],
      "expert_ids": [
        "burkhard-rost",
        "christian-dallago"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 5,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "ProtTrans + FLIP.",
      "initiatives_hosted": [
        "flip"
      ],
      "composite_score": 80.2
    },
    {
      "id": "isomorphic-labs",
      "name": "Isomorphic Labs",
      "kind": "company",
      "type": "industry",
      "country": "UK",
      "website": "https://www.isomorphiclabs.com/",
      "github": "N/A",
      "primary_benchmarks": [
        "plinder"
      ],
      "expert_ids": [
        "max-jaderberg"
      ],
      "rubric": {
        "output_volume": 2,
        "quality_median": 5,
        "breadth": 3,
        "openness": 4,
        "industry_uptake": 5,
        "longevity": 3,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Alphabet drug-discovery spinout. AlphaFold3 (preview) + PLINDER collaborator.",
      "initiatives_hosted": [],
      "composite_score": 80.0
    },
    {
      "id": "czi-science",
      "name": "CZI Science",
      "kind": "consortium",
      "type": "nonprofit",
      "country": "USA",
      "website": "https://chanzuckerberg.com/science/",
      "github": "https://github.com/chanzuckerberg",
      "primary_benchmarks": [
        "cz-virtual-cell-challenge",
        "openproblems-perturbation"
      ],
      "expert_ids": [
        "ambrose-carr"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 4,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 4,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Virtual Cellell + Open Problems funder.",
      "initiatives_hosted": [
        "czi-virtual-cell"
      ],
      "composite_score": 80.0
    },
    {
      "id": "matter-lab-toronto",
      "name": "Matter Lab (UToronto)",
      "kind": "lab",
      "type": "academic",
      "country": "Canada",
      "website": "https://www.matter.toronto.edu/",
      "github": "https://github.com/aspuru-guzik-group",
      "primary_benchmarks": [
        "moses"
      ],
      "expert_ids": [
        "alan-aspuru-guzik"
      ],
      "rubric": {
        "output_volume": 3,
        "quality_median": 4,
        "breadth": 4,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "Self-driving labs + generative chemistry.",
      "initiatives_hosted": [],
      "composite_score": 79.0
    },
    {
      "id": "alquraishi-lab",
      "name": "AlQuraishi Lab (Columbia)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://www.dbmi.columbia.edu/profile/mohammed-alquraishi/",
      "github": "https://github.com/aqlaboratory",
      "primary_benchmarks": [
        "flip"
      ],
      "expert_ids": [
        "mohammed-alquraishi"
      ],
      "rubric": {
        "output_volume": 2,
        "quality_median": 5,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 4,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "End-to-end differentiable protein ML + FLIP.",
      "initiatives_hosted": [
        "flip"
      ],
      "composite_score": 78.6
    },
    {
      "id": "nci-cptac",
      "name": "NCI CPTAC",
      "kind": "consortium",
      "type": "government",
      "country": "USA",
      "website": "https://proteomics.cancer.gov/programs/cptac",
      "github": "https://github.com/PayneLab",
      "primary_benchmarks": [
        "cptac-proteogenomic"
      ],
      "expert_ids": [],
      "rubric": {
        "output_volume": 2,
        "quality_median": 5,
        "breadth": 3,
        "openness": 4,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Oncology proteogenomics.",
      "initiatives_hosted": [
        "cptac"
      ],
      "composite_score": 78.3
    },
    {
      "id": "epa-ccte",
      "name": "US EPA CCTE",
      "kind": "lab",
      "type": "government",
      "country": "USA",
      "website": "https://www.epa.gov/chemical-research",
      "github": "N/A",
      "primary_benchmarks": [
        "toxcast"
      ],
      "expert_ids": [
        "richard-judson"
      ],
      "rubric": {
        "output_volume": 2,
        "quality_median": 4,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "ToxCast + ComptoxAI.",
      "initiatives_hosted": [],
      "composite_score": 77.4
    },
    {
      "id": "pfizer",
      "name": "Pfizer",
      "kind": "company",
      "type": "industry",
      "country": "USA",
      "website": "https://www.pfizer.com/",
      "github": "N/A",
      "primary_benchmarks": [
        "pkpd-obach"
      ],
      "expert_ids": [
        "scott-obach"
      ],
      "rubric": {
        "output_volume": 1,
        "quality_median": 5,
        "breadth": 2,
        "openness": 3,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Obach dataset provenance + major pharma sponsor.",
      "initiatives_hosted": [],
      "composite_score": 77.1
    },
    {
      "id": "uc-berkeley",
      "name": "UC Berkeley (Song / Abbeel)",
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      "type": "academic",
      "country": "USA",
      "website": "https://www.berkeley.edu/",
      "github": "https://github.com/songlab-cal",
      "primary_benchmarks": [
        "tape"
      ],
      "expert_ids": [
        "pieter-abbeel",
        "yun-song"
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      "rubric": {
        "output_volume": 2,
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        "breadth": 3,
        "openness": 5,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "TAPE + RL foundations.",
      "initiatives_hosted": [],
      "composite_score": 77.1
    },
    {
      "id": "osp-consortium",
      "name": "Open Systems Pharmacology consortium",
      "kind": "consortium",
      "type": "nonprofit",
      "country": "International",
      "website": "https://www.open-systems-pharmacology.org/",
      "github": "https://github.com/Open-Systems-Pharmacology",
      "primary_benchmarks": [
        "pksim"
      ],
      "expert_ids": [],
      "rubric": {
        "output_volume": 2,
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        "openness": 5,
        "industry_uptake": 4,
        "longevity": 4,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Open PBPK counterweight to Simcyp.",
      "initiatives_hosted": [],
      "composite_score": 75.0
    },
    {
      "id": "openproblems",
      "name": "Open Problems Consortium",
      "kind": "consortium",
      "type": "academic",
      "country": "International",
      "website": "https://openproblems.bio/",
      "github": "https://github.com/openproblems-bio",
      "primary_benchmarks": [
        "openproblems-perturbation"
      ],
      "expert_ids": [
        "fabian-theis",
        "daniel-burkhardt",
        "malte-luecken"
      ],
      "rubric": {
        "output_volume": 3,
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        "industry_uptake": 3,
        "longevity": 4,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "Canonical single-cell benchmarking consortium.",
      "initiatives_hosted": [
        "openproblems"
      ],
      "composite_score": 74.8
    },
    {
      "id": "fu-lab",
      "name": "Fu Lab (RPI)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://futianfan.github.io/",
      "github": "https://github.com/futianfan",
      "primary_benchmarks": [
        "hint-trialbench",
        "top-benchmark",
        "pmo"
      ],
      "expert_ids": [
        "tianfan-fu"
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      "rubric": {
        "output_volume": 3,
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        "openness": 5,
        "industry_uptake": 3,
        "longevity": 4,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Clinical trial + molecular optimization ML.",
      "initiatives_hosted": [
        "trialbench"
      ],
      "composite_score": 74.5
    },
    {
      "id": "certara",
      "name": "Certara / Simcyp",
      "kind": "company",
      "type": "industry",
      "country": "UK/USA",
      "website": "https://www.certara.com/",
      "github": "N/A",
      "primary_benchmarks": [
        "simcyp-validation"
      ],
      "expert_ids": [
        "amin-rostami-hodjegan"
      ],
      "rubric": {
        "output_volume": 1,
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        "breadth": 2,
        "openness": 2,
        "industry_uptake": 5,
        "longevity": 5,
        "translational_signal": 5
      },
      "flags": [],
      "notes": "Simcyp PBPK industry standard.",
      "initiatives_hosted": [],
      "composite_score": 74.5
    },
    {
      "id": "sun-lab-gatech",
      "name": "Sun Lab (Georgia Tech)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://www.sunlab.org/",
      "github": "https://github.com/sunlabgt",
      "primary_benchmarks": [
        "hint-trialbench"
      ],
      "expert_ids": [
        "jimeng-sun"
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      "rubric": {
        "output_volume": 3,
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        "industry_uptake": 3,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "Healthcare ML.",
      "initiatives_hosted": [
        "trialbench"
      ],
      "composite_score": 74.3
    },
    {
      "id": "vector-institute",
      "name": "Vector Institute",
      "kind": "consortium",
      "type": "academic",
      "country": "Canada",
      "website": "https://vectorinstitute.ai/",
      "github": "N/A",
      "primary_benchmarks": [
        "scgpt-bench"
      ],
      "expert_ids": [
        "bo-wang",
        "alan-aspuru-guzik"
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      "rubric": {
        "output_volume": 3,
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        "openness": 4,
        "industry_uptake": 4,
        "longevity": 4,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "Canadian ML for health hub.",
      "initiatives_hosted": [],
      "composite_score": 74.0
    },
    {
      "id": "cafa-consortium",
      "name": "CAFA Consortium",
      "kind": "consortium",
      "type": "academic",
      "country": "International",
      "website": "https://biofunctionprediction.org/",
      "github": "N/A",
      "primary_benchmarks": [
        "cafa5"
      ],
      "expert_ids": [
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        "pinder"
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      "github": "N/A",
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      "expert_ids": [
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      ],
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    },
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      "website": "https://bioinfo-pharma.u-strasbg.fr/",
      "github": "N/A",
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      ],
      "expert_ids": [
        "didier-rognan"
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      "website": "https://deepchem.io/",
      "github": "https://github.com/deepchem/deepchem",
      "primary_benchmarks": [
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        "clintox"
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      "expert_ids": [
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      },
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      "initiatives_hosted": [
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        "moleculenet"
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      "composite_score": 72.4
    },
    {
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      "flags": [
        "self_referential"
      ],
      "notes": "scGPT. Flag: self-referential eval.",
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    },
    {
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      "country": "USA",
      "website": "https://gladstone.org/",
      "github": "N/A",
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        "geneformer-bench"
      ],
      "expert_ids": [
        "christina-theodoris"
      ],
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      },
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    },
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      "website": "http://english.simm.cas.cn/",
      "github": "N/A",
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        "pdbbind",
        "casf-2016"
      ],
      "expert_ids": [
        "renxiao-wang"
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      "composite_score": 71.2
    },
    {
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      "type": "industry",
      "country": "USA",
      "website": "https://relaytx.com/",
      "github": "N/A",
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        "ord-bench"
      ],
      "expert_ids": [
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    },
    {
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      "github": "https://github.com/Graylab",
      "primary_benchmarks": [
        "iglm-bench"
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      "expert_ids": [
        "jeffrey-gray"
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      },
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    },
    {
      "id": "tue-eindhoven",
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      "country": "Netherlands",
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      "primary_benchmarks": [
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      "expert_ids": [
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        "industry_uptake": 3,
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      },
      "flags": [],
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      "composite_score": 69.5
    },
    {
      "id": "benevolent-ai",
      "name": "BenevolentAI",
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      "type": "industry",
      "country": "UK",
      "website": "https://www.benevolent.com/",
      "github": "https://github.com/BenevolentAI",
      "primary_benchmarks": [
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      "expert_ids": [
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        "marwin-segler"
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        "longevity": 4,
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      "flags": [],
      "notes": "GuacaMol home.",
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      "composite_score": 68.8
    },
    {
      "id": "cambridge-ml",
      "name": "Hern\u00e1ndez-Lobato Group (Cambridge)",
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      "type": "academic",
      "country": "UK",
      "website": "https://mlg.eng.cam.ac.uk/hernandez-lobato/",
      "github": "https://github.com/cambridge-mlg",
      "primary_benchmarks": [
        "dockstring"
      ],
      "expert_ids": [
        "jose-miguel-hernandez-lobato"
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      "rubric": {
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        "openness": 5,
        "industry_uptake": 3,
        "longevity": 4,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "ML for chemistry \u2014 BO + generative.",
      "initiatives_hosted": [],
      "composite_score": 68.6
    },
    {
      "id": "shoichet-lab-ucsf",
      "name": "Shoichet Lab (UCSF)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://bkslab.org/",
      "github": "https://github.com/shoichet-lab",
      "primary_benchmarks": [
        "dude"
      ],
      "expert_ids": [
        "brian-shoichet",
        "john-irwin"
      ],
      "rubric": {
        "output_volume": 2,
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        "breadth": 2,
        "openness": 5,
        "industry_uptake": 3,
        "longevity": 5,
        "translational_signal": 4
      },
      "flags": [],
      "notes": "ZINC + DUD-E. Flag: DUD-E known bias.",
      "initiatives_hosted": [],
      "composite_score": 68.3
    },
    {
      "id": "univ-lille",
      "name": "Univ Lille (Lensink)",
      "kind": "lab",
      "type": "academic",
      "country": "France",
      "website": "https://www.univ-lille.fr/",
      "github": "N/A",
      "primary_benchmarks": [
        "capri-benchmark"
      ],
      "expert_ids": [
        "marc-lensink"
      ],
      "rubric": {
        "output_volume": 1,
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        "industry_uptake": 4,
        "longevity": 4,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "CAPRI assessment.",
      "initiatives_hosted": [
        "capri"
      ],
      "composite_score": 66.7
    },
    {
      "id": "ibm-brussels",
      "name": "IBM Research Europe (CAPRI-era)",
      "kind": "lab",
      "type": "industry",
      "country": "Belgium",
      "website": "N/A",
      "github": "N/A",
      "primary_benchmarks": [
        "capri-benchmark"
      ],
      "expert_ids": [
        "shoshana-wodak"
      ],
      "rubric": {
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        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "CAPRI origin.",
      "initiatives_hosted": [
        "capri"
      ],
      "composite_score": 66.4
    },
    {
      "id": "ml2health",
      "name": "ML2Health",
      "kind": "consortium",
      "type": "academic",
      "country": "USA",
      "website": "https://github.com/ML2Health",
      "github": "https://github.com/ML2Health",
      "primary_benchmarks": [
        "ctod"
      ],
      "expert_ids": [],
      "rubric": {
        "output_volume": 2,
        "quality_median": 4,
        "breadth": 3,
        "openness": 5,
        "industry_uptake": 3,
        "longevity": 3,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "Clinical trial ML benchmarks.",
      "initiatives_hosted": [
        "trialbench"
      ],
      "composite_score": 66.2
    },
    {
      "id": "songlab",
      "name": "Song Lab (Berkeley)",
      "kind": "lab",
      "type": "academic",
      "country": "USA",
      "website": "https://people.eecs.berkeley.edu/~yss/",
      "github": "https://github.com/songlab-cal",
      "primary_benchmarks": [
        "tape"
      ],
      "expert_ids": [
        "yun-song"
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      "rubric": {
        "output_volume": 1,
        "quality_median": 4,
        "breadth": 2,
        "openness": 5,
        "industry_uptake": 4,
        "longevity": 5,
        "translational_signal": 2
      },
      "flags": [],
      "notes": "TAPE host.",
      "initiatives_hosted": [],
      "composite_score": 65.7
    },
    {
      "id": "pku-aidd",
      "name": "PKU AIDD / SIMM CAS / Tsinghua",
      "kind": "consortium",
      "type": "academic",
      "country": "China",
      "website": "https://aidd.pku.edu.cn/",
      "github": "https://github.com/pku-aidd",
      "primary_benchmarks": [],
      "expert_ids": [
        "jianfeng-pei",
        "luhua-lai",
        "jianzhu-ma"
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      "rubric": {
        "output_volume": 3,
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        "breadth": 3,
        "openness": 4,
        "industry_uptake": 3,
        "longevity": 4,
        "translational_signal": 3
      },
      "flags": [
        "self_referential"
      ],
      "notes": "Chinese AIDD benchmark ecosystem. Flag partial self-reference (HelixFold on its own bench).",
      "initiatives_hosted": [
        "pku-aidd"
      ],
      "composite_score": 65.0
    },
    {
      "id": "clawbio",
      "name": "ClawBio",
      "kind": "consortium",
      "type": "nonprofit",
      "country": "International",
      "website": "https://clawbio.ai/",
      "github": "https://github.com/ClawBio/ClawBio",
      "primary_benchmarks": [
        "clawbio-bench"
      ],
      "expert_ids": [],
      "rubric": {
        "output_volume": 2,
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        "industry_uptake": 2,
        "longevity": 2,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "Open-source bio-skill consortium. Third-party correctness benchmark distinguishes it.",
      "initiatives_hosted": [
        "clawbio"
      ],
      "composite_score": 64.3
    },
    {
      "id": "biostochastics",
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      "type": "industry",
      "country": "USA",
      "website": "https://github.com/biostochastics",
      "github": "https://github.com/biostochastics/clawbio_bench",
      "primary_benchmarks": [
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      "expert_ids": [],
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        "breadth": 2,
        "openness": 5,
        "industry_uptake": 2,
        "longevity": 2,
        "translational_signal": 3
      },
      "flags": [],
      "notes": "Independent benchmark author for ClawBio \u2014 structurally non-self-referential audit.",
      "initiatives_hosted": [
        "clawbio"
      ],
      "composite_score": 59.3
    },
    {
      "id": "medbioinformatics",
      "name": "MedBioinformatics Solutions",
      "kind": "company",
      "type": "industry",
      "country": "Spain",
      "website": "https://www.disgenet.com/",
      "github": "N/A",
      "primary_benchmarks": [
        "disgenet"
      ],
      "expert_ids": [
        "laura-furlong"
      ],
      "rubric": {
        "output_volume": 1,
        "quality_median": 3,
        "breadth": 2,
        "openness": 2,
        "industry_uptake": 4,
        "longevity": 4,
        "translational_signal": 3
      },
      "flags": [
        "license-gated-commercial"
      ],
      "notes": "DisGeNET commercial spinout.",
      "initiatives_hosted": [],
      "composite_score": 55.2
    },
    {
      "id": "tuebingen-boeckler",
      "name": "T\u00fcbingen (Boeckler Lab)",
      "kind": "lab",
      "type": "academic",
      "country": "Germany",
      "website": "https://www.uni-tuebingen.de/",
      "github": "N/A",
      "primary_benchmarks": [
        "dekois"
      ],
      "expert_ids": [],
      "rubric": {
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        "openness": 4,
        "industry_uptake": 3,
        "longevity": 4,
        "translational_signal": 2
      },
      "flags": [],
      "notes": "DEKOIS.",
      "initiatives_hosted": [],
      "composite_score": 54.0
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  ]
}