Search the knowledge graph for drug targets by gene symbol or keyword.

Returns matching targets with basic metadata. Optionally filter by therapeutic indication. Use this as the starting point to explore targets.

Returns: JSON list of matching targets with gene_symbol, name, target_class, and counts of related compounds, patents, and papers.

Get a comprehensive intelligence dossier for a drug target.

Returns UniProt biology, target scores, SAR summary, disease associations, validation evidence, pathways, PPIs, competitive landscape, clinical pipeline, and publication momentum. This is the primary tool for any target question.

Get compounds active against a specific drug target.

Returns compounds with activity data (IC50, Ki, etc.) sorted by potency.

Get patents mentioning a specific drug target.

Returns patent filings with titles, dates, and assignee organizations.

Get scientific papers mentioning a specific drug target.

Returns publications from PubMed/OpenAlex with titles and dates.

Get the AlphaFold structural snapshot for a drug target.

Returns AlphaFold model URLs (PDB / CIF / PAE), per-residue confidence summary (mean pLDDT, fractions of residues at high / confident / low confidence, disordered fraction), and protein length. Useful for SBDD scoping, disorder/IDR risk, and confidence-aware target triage. Pair with mosaic_assess_druggability for binding-pocket scoring.

Assess structural druggability of a target from AlphaFold + fpocket.

Returns the top binding pockets (volume, druggability score), pocket count, and a coarse structural_tier (highly_druggable / druggable / challenging / undruggable) along with a plain-English interpretation. This is the structural answer to "is this target small-molecule tractable?" — orthogonal to literature-derived druggability heuristics.

Get the full competitive landscape for a drug target.

Multi-hop traversal: Target <- Compounds, Target <- Patents -> Organizations. Shows which pharma/biotech companies are active on this target, how many patents and compounds each has, and overall competitive intensity.

Get pathway context for a drug target.

Shows which biological pathways the target participates in, other targets in the same pathways, and protein-protein interactions.

Get the selectivity profile of a compound across all targets.

Shows activity values against every target the compound has been tested on. Critical for assessing off-target effects and safety liability.

Get the full therapeutic landscape for a disease indication.

Shows all targets implicated in this indication, compounds in development, and clinical status.

List all therapeutic indications available in the knowledge graph.

Returns every indication with the number of associated targets. Use this to discover which disease areas are loaded.

List fine-grained oncology sub-indications in the knowledge graph.

Sub-indications are histology- or biomarker-defined cancer subtypes (e.g. 'EGFR-mutant NSCLC', 'triple-negative breast cancer') organized under broader parent indications. Optionally pass parent_indication (id, name, or synonym, e.g. 'lung cancer' or 'NSCLC') to list only its children. Each entry includes the number of linked targets.

Break a target's oncology associations down by sub-indication.

For the given gene, returns the most relevant cancer sub-indications (e.g. EGFR -> NSCLC subtypes vs. colorectal) with the evidence type and confidence of each link. Complements mosaic_get_target_profile with finer indication granularity.

Request a target Mosaic does not yet cover.

Use this when a gene is outside the current covered set so the operator can prioritise it in the next ingestion batch. Idempotent: re-requesting the same gene/email bumps a counter. Also returns the closest covered targets so the user still gets a useful answer.

Create a watchlist to track targets, indications, orgs, or compounds.

owner_key is the user id, email, or an 'anon:' for anonymous sessions. Returns the new watchlist id to use with mosaic_watchlist_add_item.

Add a watched entity (target/indication/organization/compound/ relation_type) to a watchlist. Idempotent — re-adding is a no-op.

Get a watchlist with its items and recent detected events.

List an owner's watchlists with item and recent-event counts.

Get computed attractiveness scores for a drug target.

Returns overall target attractiveness, scientific validation, druggability, competitive intensity, and research momentum (0-1 scale) with direction.

Get experimental validation evidence for a drug target.

Returns genetic (CRISPR/siRNA), in vivo (animal models), clinical (patient data), and pharmacological validation evidence from literature. Includes specific papers with model systems and outcomes.

Get clinical trial pipeline for compounds targeting a gene.

Returns compounds in clinical development with indications, trial phases, and status from ClinicalTrials.gov data.

Get structural analogs of a compound with Tanimoto similarity.

Returns analogs with similarity scores, shared scaffolds, and their activity against targets. Useful for SAR analysis and lead optimization.

Side-by-side comparison of 2-5 drug targets.

Returns compound counts, patent counts, paper counts, best IC50, max clinical phase, attractiveness scores, and momentum for each target.

Find underexplored high-potential drug targets — white-space opportunities.

Identifies targets with high scientific validation but low competitive intensity. These are the best opportunities for novel drug programs where the biology is strong but Big Pharma hasn't crowded the space.

Ranked by opportunity_score = validation × (1 - competition) × momentum_boost.

Find validated targets that are structurally hard to hit with small molecules.

Returns targets in the 'challenging' or 'undruggable' tier (or with a top fpocket druggability score below the threshold), plus their pipeline gap signals (compound count, approved drug count, validation count) and a suggested modality (PROTAC / glue, biologic / PPI, fragment-based, or allosteric SBDD). This is the white-space tool for new-modality programs.

Ranked by opportunity_score = validation × (1 - top_pocket_score) × (1 - competitive_intensity).

Find synthetic-lethal whitespace: targets functionally coupled to a developed (drugged) target but themselves undeveloped.

For an anchor target with chemical matter, surfaces partners that share STRING protein-protein interactions and/or Reactome pathways with it yet have < 5 patents and no clinical compound — i.e. strong biological coupling, low competitive activity. Ranked by a whitespace score and returned with a deterministic suggested experimental approach.

Scope: co_functionality_proxy is a PPI + shared-pathway proxy for co-essentiality, NOT DepMap co-essentiality (DepMap ingestion is a later sprint). Treat it as a hypothesis generator, not evidence.

Which compound modalities are explored vs absent for a target.

Modality is a heuristic SMILES classification (small_molecule, covalent, degrader, macrocycle, peptide_like) over the top-ranked compounds — partial coverage by design; unclassified is reported explicitly. Target-level only: protein_family is not populated, so family-level rollups are unavailable (stated, not silently wrong).

Candidate resistance-bypass / escape targets for a given target.

From a deterministic keyword pass over the literature (resistance_relations — GLiREL has no resistance edge type), surfaces targets co-mentioned with the query target in resistance-context abstracts, ranked by a drugability-gap score (strong resistance evidence, low development activity). Hypothesis generator, not evidence — every row carries its source snippet.

Who works on a target, recency-weighted, and what else they work on — a talent-flow signal.

Surfaces the most active researchers on a target (publication-based, using the resolved persons table) and, for each, the other targets they've published on over time — i.e. "people who worked on X now also on Y". Patent inventors are not included (not ingested).

Targets whose recent literature/patent activity significantly exceeds their own prior baseline (z-score > 2).

Simple statistics over monthly counts — no ML. Use to spot targets heating up before they crowd. sparkline is recent monthly counts.

Structurally similar targets to a given gene, ranked by Foldseek TM-score over the AlphaFold PDB corpus.

Returns the top-k neighbours (default 10) with neighbour metadata (name, target_class, druggability_tier) and the structural-similarity metrics (tm_score normalised over query length, alntmscore over alignment length, evalue, lddt, rmsd). Use for paralog / fold-analog discovery, scaffold-hopping target ideation, and cross-family chemistry repurposing.

Source table is populated by scripts/build_foldseek_index.py. When the index hasn't been built yet, returns an empty result with a populate-hint rather than failing — degrades cleanly on fresh DBs.

Get a pharma/biotech organization's full portfolio.

Shows which drug targets they're active on, their patent filings, therapy area focus, and competitive positioning. Use to understand what a company is working on and where they're investing.

Get the full knowledge graph network around a drug target.

Returns all connected entities (compounds, diseases, pathways, organizations, interacting proteins) as nodes and edges. Shows how a target connects to the broader drug discovery landscape.

Useful for understanding the full context of a target and finding non-obvious connections.

Get the mechanism-of-action profile for a drug target.

Returns how compounds interact with this target — inhibitors (covalent, allosteric, competitive), agonists, antagonists, degraders (PROTAC). Also shows semantic edge types: validation evidence, resistance mechanisms, biomarker roles, safety concerns, and clinical efficacy signals.

Extracted by GLiREL from paper and patent abstracts.

Get the full evidence landscape for a drug target from semantic extraction.

Shows all relation types (validation, resistance, biomarker, safety, efficacy, expression, pathway, drug target ID) broken down by source type (paper vs patent), with confidence stats and top evidence snippets per relation type.

Use this to understand the strength and breadth of evidence for a target.

Search the entire knowledge graph for entity pairs with a specific relation type.

Returns the highest-confidence entity pairs for a given relation (e.g. all 'degrades_protac' relations, or all 'resistance_mechanism' edges). Useful for cross-target analysis like "which targets have PROTAC degraders?" or "where are resistance mechanisms documented?"

Get the polypharmacology profile of a compound — all targets it interacts with.

Shows every target the compound has semantic relations with, the mechanism of action for each (inhibits, agonizes, degrades, etc.), and evidence counts. Useful for understanding off-target effects, repurposing potential, and selectivity from a semantic (not just activity) perspective.

Get overall statistics for the Mosaic knowledge graph.

Returns entity counts (targets, compounds, papers, patents), semantic relation totals and breakdown by type, coverage metrics, and ChEMBL activity counts. Use this to understand the scope and coverage of the KG.

Return real ClinicalTrials.gov records (NCT ID, title, phase, sponsor, status).

Filters by any combination of gene symbol, compound name, or indication. Unlike mosaic_clinical_pipeline which can synthesize from max_phase, every row returned here has a real NCT ID and brief title. Use this when you need to cite specific trials or highlight read-outs.

Query openFDA for a drug's approval status, label indications, and adverse events.

Returns: FDA approval dates, brand/generic names, sponsor, product type, route of administration, and a summary count of serious adverse events. Data is live from openFDA — does not require local ingestion.

Side-by-side comparison of two compounds.

Returns max_phase, first approval year, molecule type, shared targets, unique targets per side, and per-target potency (pChEMBL) for both. Use this for competitive analyses like "osimertinib vs erlotinib" or "imatinib vs dasatinib".

Find new indications where a compound's primary targets are implicated.

Returns indications where the compound's targets have supporting evidence but the compound itself is not yet clinically active. Ranked by a simple target-support × avg-evidence score. Useful as a starting point for repurposing hypotheses — not a substitute for clinical review.

Rank the top authors (KOLs) publishing on a target or indication.

Scores by paper volume with recency weighting (papers since 2023 count 2x). Returns person name, paper count, most recent publication date, and affiliated organizations. Useful for advisory board assembly, trial PI scouting, and competitive intelligence.