mosaic for drug target intelligence

mosaic_search_targets

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.

mosaic_get_target_profile

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.

mosaic_get_target_compounds

Get compounds active against a specific drug target. Returns compounds with activity data (IC50, Ki, etc.) sorted by potency.

mosaic_get_target_patents

Get patents mentioning a specific drug target. Returns patent filings with titles, dates, and assignee organizations.

mosaic_get_target_papers

Get scientific papers mentioning a specific drug target. Returns publications from PubMed/OpenAlex with titles and dates.

mosaic_get_target_structure

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.

mosaic_assess_druggability

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.

mosaic_competitive_landscape

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.

mosaic_pathway_context

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.

mosaic_compound_selectivity

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.

mosaic_indication_landscape

Get the full therapeutic landscape for a disease indication. Shows all targets implicated in this indication, compounds in development, and clinical status.

mosaic_list_indications

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.

mosaic_list_subindications

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.

mosaic_subindication_breakdown

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.

mosaic_target_wishlist_add

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.

mosaic_watchlist_create

Create a watchlist to track targets, indications, orgs, or compounds. owner_key is the user id, email, or an 'anon:<token>' for anonymous sessions. Returns the new watchlist id to use with mosaic_watchlist_add_item.

mosaic_watchlist_add_item

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

mosaic_watchlist_get

Get a watchlist with its items and recent detected events.

mosaic_watchlist_list

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

mosaic_target_scores

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.

mosaic_target_validation

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.

mosaic_clinical_pipeline

Get clinical trial pipeline for compounds targeting a gene. Returns compounds in clinical development with indications, trial phases, and status from ClinicalTrials.gov data.

mosaic_compound_analogs

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.

mosaic_compare_targets

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.

mosaic_find_opportunities

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.

mosaic_find_undruggable_targets

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…

mosaic_synthetic_lethal_whitespace

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.…

mosaic_modality_gaps

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).

mosaic_resistance_bypass_map

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.

mosaic_talent_migration

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).

mosaic_emerging_signals

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.

mosaic_find_similar_targets

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. Sour…

mosaic_org_portfolio

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.

mosaic_target_network

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.

mosaic_target_mechanisms

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.

mosaic_evidence_map

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.

mosaic_relation_search

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?"

mosaic_compound_polypharmacology

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.

mosaic_kg_stats

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.

mosaic_trial_results

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.