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Nullary - Negative Results Intelligence For Drug Discovery

Nullary - Negative Results Intelligence For Drug Discovery

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github.com/nullary-aiHealth1Updated 22w ago
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About Nullary - Negative Results Intelligence For Drug Discovery

Nullary is a hosted MCP server for querying measured negative results in drug discovery, with provenance for each finding. It is for researchers and AI agents and covers multiple modalities, targets, indications, and compounds.

Key features

  1. 01Search failures across drug modalities
  2. 02Review target histories across modalities
  3. 03Retrieve compound negative profiles
  4. 04Return finding provenance and sources
  5. 05Inspect model registry and coverage

Use cases

  1. 01Assess prior failures for a drug target
  2. 02Find terminated trials and their reasons
  3. 03Verify negative findings and their sources for a compound

MCP support

Nullary is a remote MCP server using streamable HTTP, with no installation or API key required. It exposes modality-organized query tools to MCP clients and includes source information with findings.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.nullary.ai/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

35
ParameterTypeRequiredDescription
tierenumNoConfidence tier. 'curated' (default) returns the credible layer; 'all' also includes high-volume screening-grade rows (PubChem HTS inactives, CRISPR non-essential tails).curated | all
limitintegerNoMaximum number of findings to return (1–100, default 25).
offsetintegerNoNumber of findings to skip before this page, for pagination (default 0).
targetstringNoUniProt accession or gene symbol
outcomestringNooutcome filter (inactive, failed_safety, terminated, …)
compoundstringNocompound name, ChEMBL ID, or InChIKey

Details

From the project README

Nullary MCP Server

Negative results intelligence for drug discovery — over the Model Context Protocol.

Nullary is a hosted MCP server that lets AI agents query measured negative results from drug discovery: inactive compounds, failed selectivity panels, terminated clinical trials, failed CRISPR screens, antibody developability failures, and more — each result carrying full provenance (source database, DOI/PMID, license).

This repository documents the public MCP interface. The server is hosted; the data pipeline and application code are maintained separately.

Connect

Nullary is a remote MCP server (streamable-HTTP) — nothing to install, no API key:

https://mcp.nullary.ai/mcp
Claude Code / generic MCP client
{
  "mcpServers": {
    "nullary": {
      "url": "https://mcp.nullary.ai/mcp"
    }
  }
}
Cursor

Settings → MCP → Add new MCP server → paste the URL above (transport: streamable-HTTP).

Claude Desktop

Settings → Connectors → Add custom connector → URL https://mcp.nullary.ai/mcp.

What you can ask

  • "What's failed against EGFR?" — failed compounds, trials, and screens for a target, across modalities
  • "Which kinase inhibitors were inactive in ChEMBL?"
  • "Show terminated Phase 2 oncology trials and why they stopped"
  • "Failed CRISPR knockouts for TP53"

Tools are organized by modality (small molecule, CRISPR, antibody, peptide, PROTAC, clinical trial, …); every response cites its source.

Tools

The server exposes 35 tools — served live via the MCP tools/list method. Full JSON Schemas (inputs per tool) are in tools.json. Tools span the seven modalities plus cross-modality history, compound/provenance lookups, and the Layer-1 model registry.

ToolDescription
search_inactive_compoundsInactive small-molecule compound-target pairs.
search_failed_selectivitySmall molecules that failed selectivity.
search_admet_failuresSmall-molecule ADMET failures.
search_failed_guidesFailed/ineffective CRISPR guides.
search_failed_essentiality_screensNon-dependency / failed essentiality screens.
search_ancestry_specific_failuresAncestry-specific CRISPR failures.
search_developability_failuresAntibody developability failures.
search_failed_clinical_antibodiesDiscontinued/terminated clinical antibodies.
search_failed_peptide_therapeuticsFailed peptide therapeutics.
search_peptide_stability_issuesPeptide stability/half-life failures.
search_failed_protacsPROTACs that failed degradation/ternary/permeability.
search_protac_e3_issuesPROTAC E3-ligase recruitment / ternary failures.
search_failed_oligonucleotidesASOs/siRNAs that failed engagement/developability.
search_oligo_delivery_failuresOligonucleotide delivery failures.
search_failed_vaccinesFailed/terminated vaccines (by pathogen/indication).
search_vaccine_immunogenicity_failuresFailed vaccine immunogen designs.
search_failed_adcsADCs that failed at any stage.
search_adc_linker_failuresADC failures attributed to linker chemistry.
search_failed_bispecificsBispecifics that failed at any stage.
search_bispecific_format_failuresBispecific format/engineering failures.
search_admet_failures_all_modalitiesADMET failures across ALL modalities.
search_drug_drug_interaction_failuresDrug-drug interaction failures.
search_mechanism_failuresApproaches that failed for a mechanism (by target).
search_failed_replicationsFindings that failed to replicate.
search_safety_failuresClinical/preclinical safety failures across modalities.
search_target_historyALL failed approaches against a target across every modality.
search_indication_historyALL failed approaches for an indication across every modality.
search_pathogen_historyVaccine + antimicrobial + antibody failures for a pathogen.
get_compoundA compound + its full negative profile across modalities/sources.
get_finding_provenanceFull provenance + detail for a single finding by id.
get_target_landscapeTarget "graveyard" / exhaustion index — how picked-over a target is, by modality and outcome.
list_top_targetsThe most heavily-pursued targets, ranked by recorded negative findings.
list_modelsSummary of the Layer-1 inactivity-scoring model registry.
get_model_cardPer-target Layer-1 model card: training counts + held-out scaffold-split metrics.
get_coveragePer-modality and per-source coverage stats.

Links

License

This documentation repository is MIT-licensed. The underlying data is provided under each source's respective license — see the coverage page.

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