{
  "generated": "2026-10-15",
  "tools": {
    "pubmed": {
      "name": "PubMed",
      "what": "41 million citations and abstracts",
      "url": "https://pubmed.ncbi.nlm.nih.gov/"
    },
    "ctgov": {
      "name": "ClinicalTrials.gov",
      "what": "600,000+ registered studies",
      "url": "https://clinicaltrials.gov/"
    },
    "opentargets": {
      "name": "Open Targets",
      "what": "target–disease evidence, assembled from genetics and literature",
      "url": "https://platform.opentargets.org/"
    },
    "chembl": {
      "name": "ChEMBL",
      "what": "2.9 million compounds and 24.5 million activity measurements",
      "url": "https://www.ebi.ac.uk/chembl/"
    },
    "pdb": {
      "name": "Protein Data Bank",
      "what": "260,000+ experimentally solved structures",
      "url": "https://www.rcsb.org/"
    },
    "alphafold": {
      "name": "AlphaFold DB",
      "what": "open predicted structures for most known proteins",
      "url": "https://alphafold.ebi.ac.uk/"
    },
    "uniprot": {
      "name": "UniProtKB",
      "what": "150 million protein sequences",
      "url": "https://www.uniprot.org/"
    },
    "boltz": {
      "name": "Boltz",
      "what": "open structure and interaction prediction",
      "url": "https://github.com/jwohlwend/boltz"
    },
    "openfold": {
      "name": "OpenFold",
      "what": "trainable protein structure prediction",
      "url": "https://github.com/aqlaboratory/openfold"
    },
    "rdkit": {
      "name": "RDKit",
      "what": "cheminformatics toolkit",
      "url": "https://github.com/rdkit/rdkit"
    },
    "vina": {
      "name": "AutoDock Vina",
      "what": "molecular docking",
      "url": "https://github.com/ccsb-scripps/AutoDock-Vina"
    },
    "gnina": {
      "name": "GNINA",
      "what": "docking scored by a neural network",
      "url": "https://github.com/gnina/gnina"
    },
    "diffdock": {
      "name": "DiffDock",
      "what": "diffusion-based docking",
      "url": "https://github.com/gcorso/DiffDock"
    },
    "openmm": {
      "name": "OpenMM",
      "what": "GPU molecular dynamics",
      "url": "https://github.com/openmm/openmm"
    },
    "reinvent": {
      "name": "REINVENT 4",
      "what": "generative molecular design",
      "url": "https://github.com/MolecularAI/REINVENT4"
    },
    "chemprop": {
      "name": "Chemprop",
      "what": "property prediction from molecular graphs",
      "url": "https://github.com/chemprop/chemprop"
    },
    "aizynth": {
      "name": "AiZynthFinder",
      "what": "retrosynthesis planning",
      "url": "https://github.com/MolecularAI/aizynthfinder"
    },
    "scanpy": {
      "name": "Scanpy",
      "what": "single-cell analysis at 100M+ cells",
      "url": "https://github.com/scverse/scanpy"
    },
    "pysb": {
      "name": "PySB",
      "what": "rule-based models of biological systems",
      "url": "https://github.com/pysb/pysb"
    },
    "tellurium": {
      "name": "Tellurium",
      "what": "simulation of biological models",
      "url": "https://github.com/sys-bio/tellurium"
    },
    "cobrapy": {
      "name": "COBRApy",
      "what": "constraint-based models of metabolism",
      "url": "https://github.com/opencobra/cobrapy"
    }
  },
  "capabilities": [
    {
      "id": "map",
      "name": "Map the disease",
      "claim": "Build the full picture of a disease, with every claim cited.",
      "text": "Reads the literature, the trial registries and the genetics, and assembles one evidence map: what is known, what is contested, what nobody has tried. Every entry carries its source, so the map can be audited line by line.",
      "crew": "reader",
      "stage": "standard",
      "takes": "A disease and the questions to answer",
      "gives": "An evidence map, every claim cited",
      "methods": [
        "Literature retrieval",
        "Claim extraction",
        "Contradiction detection",
        "Evidence grading"
      ],
      "stack": [
        "pubmed",
        "ctgov",
        "opentargets"
      ],
      "gate": "A researcher reviews the map",
      "cost": "Hours of compute"
    },
    {
      "id": "watch",
      "name": "Watch the field",
      "claim": "Catch the paper that changes everything, the day it lands.",
      "text": "Every new paper, preprint and trial registration in the area is read as it appears and checked against the project's live hypotheses. When one contradicts an assumption the project rests on, the team hears in hours, not at the next review.",
      "crew": "reader",
      "stage": "standard",
      "takes": "The project's hypotheses and the fields to watch",
      "gives": "An alert: the paper, and what it threatens",
      "methods": [
        "Continuous monitoring",
        "Semantic matching",
        "Contradiction detection",
        "Alerting"
      ],
      "stack": [
        "pubmed",
        "ctgov",
        "opentargets"
      ],
      "gate": "A researcher decides what it changes",
      "cost": "Minutes of compute a day"
    },
    {
      "id": "failures",
      "name": "Learn from the failures",
      "claim": "Read the dead ends, so the project does not walk into them again.",
      "text": "Most of what has been tried did not work, and much of it was never written up well. The agents comb the failed trials, the negative results and the retractions around a target, and say plainly what has already been ruled out and why.",
      "crew": "reader",
      "stage": "emerging",
      "takes": "A target, mechanism or compound class",
      "gives": "What failed before, and the reason given",
      "methods": [
        "Negative-result mining",
        "Trial outcome analysis",
        "Retraction checking",
        "Failure-mode clustering"
      ],
      "stack": [
        "ctgov",
        "pubmed",
        "chembl"
      ],
      "gate": "Reviewed before a direction is dropped",
      "cost": "Hours of compute"
    },
    {
      "id": "targets",
      "name": "Rank the targets",
      "claim": "Score every protein in the disease for how good a target it would make.",
      "text": "Pulls together genetic association, expression, pathway position and what has already been tried, then ranks the candidate targets and says, for each one, what evidence supports it and what argues against it.",
      "crew": "inventor",
      "stage": "standard",
      "takes": "The evidence map and the disease biology",
      "gives": "Ranked targets, with evidence and risks",
      "methods": [
        "Target–disease association scoring",
        "Pathway analysis",
        "Tractability assessment",
        "Prior-art search"
      ],
      "stack": [
        "opentargets",
        "uniprot",
        "pubmed"
      ],
      "gate": "A researcher picks what goes forward",
      "cost": "Hours of compute"
    },
    {
      "id": "structure",
      "name": "Model the target",
      "claim": "Get a 3D model of the protein, and find the pocket a drug could bind.",
      "text": "Starts from the experimental structure where one exists and a predicted structure where it does not, then finds the binding sites, checks whether they are druggable and flags where the model is least certain.",
      "crew": "analyst",
      "stage": "standard",
      "takes": "A protein sequence or structure",
      "gives": "A structure, its pockets and its confidence",
      "methods": [
        "Structure prediction",
        "Pocket detection",
        "Druggability scoring",
        "Confidence estimation"
      ],
      "stack": [
        "pdb",
        "alphafold",
        "boltz",
        "openfold"
      ],
      "gate": "A structural biologist sees the shaky ones",
      "cost": "GPU hours"
    },
    {
      "id": "screen",
      "name": "Screen the chemical space",
      "claim": "Search enormous compound libraries for molecules that could bind.",
      "text": "Filters vast catalogues down to the plausible, docks the survivors against the pocket, and scores them for how well they fit. The answer is a short, ranked list a chemist can actually act on, with each pose kept for inspection.",
      "crew": "analyst",
      "stage": "emerging",
      "takes": "A pocket, and the libraries to search",
      "gives": "A ranked shortlist, with every pose kept",
      "methods": [
        "Virtual screening",
        "Molecular docking",
        "Pose scoring",
        "Hit triage"
      ],
      "stack": [
        "chembl",
        "rdkit",
        "vina",
        "gnina",
        "diffdock"
      ],
      "gate": "A chemist reviews it before anything is ordered",
      "cost": "GPU days for a large library"
    },
    {
      "id": "simulate",
      "name": "Simulate the binding",
      "claim": "Watch a molecule and its target move, atom by atom.",
      "text": "Docking says whether a molecule fits; simulation says whether it stays. Molecular dynamics runs the pair in water over time, so a pose that falls apart in the first nanoseconds is dropped before anyone spends a bench hour on it.",
      "crew": "analyst",
      "stage": "emerging",
      "takes": "A protein, a molecule and a docked pose",
      "gives": "How steadily it binds, and what holds it",
      "methods": [
        "Molecular dynamics",
        "Free-energy estimation",
        "Pose stability analysis",
        "Interaction fingerprinting"
      ],
      "stack": [
        "openmm",
        "rdkit",
        "pdb"
      ],
      "gate": "The Skeptic re-runs it from elsewhere",
      "cost": "GPU days per molecule"
    },
    {
      "id": "disease",
      "name": "Simulate the disease",
      "claim": "Run the intervention on a model of the disease before anyone touches a pipette.",
      "text": "Builds a working model of the pathway the disease runs on, switches the target off in it, and watches what happens downstream: what recovers, what breaks, who it would help. A cheap way to find out which experiment is worth paying for.",
      "crew": "analyst",
      "stage": "frontier",
      "takes": "A pathway and the intervention to try",
      "gives": "The downstream effects, and what to test",
      "methods": [
        "Pathway modelling",
        "In-silico knockdown",
        "Sensitivity analysis",
        "Virtual cohorts"
      ],
      "stack": [
        "pysb",
        "tellurium",
        "cobrapy",
        "opentargets"
      ],
      "gate": "A hypothesis only, until a lab says otherwise",
      "cost": "Hours to days of compute"
    },
    {
      "id": "design",
      "name": "Design new molecules",
      "claim": "Invent molecules that do not exist yet, built to fit the pocket.",
      "text": "When nothing in the catalogues fits, the agents generate new structures against the pocket and optimise them for potency, selectivity and the properties a drug needs, keeping only what a chemist could actually make.",
      "crew": "inventor",
      "stage": "frontier",
      "takes": "A pocket and the properties to hit",
      "gives": "New structures, with a route to make them",
      "methods": [
        "Generative molecular design",
        "Multi-objective optimisation",
        "Property prediction",
        "Retrosynthesis"
      ],
      "stack": [
        "reinvent",
        "chemprop",
        "aizynth",
        "rdkit"
      ],
      "gate": "A chemist confirms the route before funding",
      "cost": "GPU days per campaign"
    },
    {
      "id": "safety",
      "name": "Predict what goes wrong",
      "claim": "Look for the toxicity and the off-targets before the lab does.",
      "text": "Most candidates fail on safety, not on potency. The agents predict toxicity, check the molecule against proteins it should not touch, and search the literature for compounds that failed the same way, so the known traps are avoided early.",
      "crew": "skeptic",
      "stage": "emerging",
      "takes": "A candidate molecule",
      "gives": "Predicted liabilities, with precedents",
      "methods": [
        "Toxicity prediction",
        "Off-target screening",
        "Precedent search",
        "Risk ranking"
      ],
      "stack": [
        "chemprop",
        "chembl",
        "rdkit",
        "pubmed"
      ],
      "gate": "A reviewer sees every flag, and can stop it",
      "cost": "Hours of compute"
    },
    {
      "id": "repurpose",
      "name": "Repurpose approved drugs",
      "claim": "Find the medicine that already exists and nobody has tried here.",
      "text": "The fastest route to a patient is a drug that is already approved. The agents match the disease mechanism against every approved compound and its known effects, then rank what comes back by plausibility and evidence.",
      "crew": "inventor",
      "stage": "standard",
      "takes": "A disease mechanism or target",
      "gives": "Ranked candidates, with the trial that tests them",
      "methods": [
        "Mechanism matching",
        "Knowledge-graph reasoning",
        "Signature comparison",
        "Evidence grading"
      ],
      "stack": [
        "chembl",
        "opentargets",
        "pubmed",
        "ctgov"
      ],
      "gate": "A clinician reviews it before it is published",
      "cost": "Hours of compute"
    },
    {
      "id": "omics",
      "name": "Analyse the data",
      "claim": "Turn raw experimental data into an answer, with the code attached.",
      "text": "Takes the data a lab produces, single-cell or otherwise, and runs the analysis: what changed, in which cells, and whether the result survives a statistical kicking. Every run is saved with its code, data and settings so it can be repeated.",
      "crew": "analyst",
      "stage": "standard",
      "takes": "Experimental data, and a question",
      "gives": "A result, with code and data attached",
      "methods": [
        "Single-cell analysis",
        "Differential expression",
        "Statistical testing",
        "Reproducible pipelines"
      ],
      "stack": [
        "scanpy",
        "opentargets"
      ],
      "gate": "The Skeptic re-runs it before publication",
      "cost": "Hours of compute"
    },
    {
      "id": "protocol",
      "name": "Write the experiment",
      "claim": "Turn a hypothesis into a protocol a lab can run on Monday.",
      "text": "A prediction is worth nothing until someone tests it. The agents write the experiment: the controls, the sample size, the readout and what result would prove the hypothesis wrong, in the form a partner lab works from.",
      "crew": "skeptic",
      "stage": "emerging",
      "takes": "A hypothesis, and the lab that would test it",
      "gives": "A protocol, with the result that kills it",
      "methods": [
        "Experimental design",
        "Power analysis",
        "Protocol generation",
        "Pre-registration"
      ],
      "stack": [
        "pubmed",
        "ctgov"
      ],
      "gate": "The lab and the lead sign it before work starts",
      "cost": "Hours of compute"
    }
  ]
}