Autonomous Asset Discovery
The best asset in your pipeline may already exist - in someone else’s archive
Autonomous Asset Discovery reads the biomedical literature, trial registries, patent records and structured bioactivity databases the way a research team would, if a research team could read all of it. It surfaces overlooked, abandoned, underdeveloped and repurposable drug assets - each one ranked, explained and traceable to the evidence behind it.
- 37M+
- Indexed literature records
- 500K+
- Registered clinical studies
- 2.4M+
- Bioactive compounds
- 20M+
- Measured bioactivities
Source corpora: PubMed, ClinicalTrials.gov, ChEMBL, Open Targets, UniProt, DrugBank, Reactome, DisGeNET, SureChEMBL and the FDA Orange Book.
Key features
Seven capabilities, one evidence structure underneath
Open a capability to see what it does and the screen it does it in. Each one queries the same graph, so an answer found in one is the same answer everywhere else in the product.
01Biomedical knowledge graph
Every entity the platform knows about - targets, diseases, compounds, pathways, trials, patents, publications, companies - is a node. Every relationship between them is an edge carrying its own provenance, confidence and date. Ask a question and you are querying an evidence structure, not a search index.
- Multi-hop traversal across target → pathway → disease → compound → trial → patent
- Provenance on every edge: you can always click through to the source record
- Continuous ingestion, so the graph reflects this week’s literature, not last year’s snapshot
- Hybrid graph engine - relational storage for authoritative records, native graph traversal for dense neighbourhood queries
Asset Discovery · Knowledge Graph ExplorerExpanded target neighbourhood, edge provenance panel open on the right. 02Drug repurposing engine
Systematic, mechanism-led matching of existing compounds - approved, shelved, withdrawn, investigational - against indications they were never developed for.
- Mechanism-of-action reasoning, not just signature similarity
- Safety and pharmacology history carried forward from the original programme
- Regulatory-path and exclusivity context surfaced alongside each candidate
- Ranked candidate sets with the reasoning chain attached
Asset Discovery · Repurposing moduleRanked candidate table with mechanism rationale expanded for the top result. 03Shelved & white-space discovery
Two of the most commercially interesting questions in discovery, answered systematically.
- Shelved asset detection - programmes that stopped without a safety signal, identified from trial-registry state changes, publication silence, patent maintenance behaviour and corporate disclosure patterns
- White-space mapping - target–indication pairs with strong biological support and no active competitive programme
- Competitive density scoring, so you can see how crowded a space is before you enter it
Asset Discovery · White-space matrixTarget × indication grid shaded by evidence strength and competitive density. 04Novel target & biomarker discovery
Target and biomarker candidates assembled from integrated evidence, each carrying the assessment that qualifies it for a programme.
- Target identification from integrated genetic association, expression, perturbation and pathway evidence
- Tractability and druggability assessment, including structural availability
- Biomarker candidate surfacing with the cohort and assay evidence behind each one
- Combination hypothesis generation across mechanism pairs
Asset Discovery · Target ExplorerIntegrated target evidence view. Asset Discovery · Tractability panelDruggability and structural availability assessment. 05Explainable opportunity ranking
No black-box scores. Every ranked opportunity opens into the reasoning that produced it.
- Transparent, inspectable scoring components you can re-weight to match your strategy
- The evidence set behind each score, at record level
- Explicit statement of what is not known - gaps are surfaced, not smoothed over
- Exportable dossiers built for an investment committee, not for a data scientist
Asset Discovery · Opportunity detailScore decomposition on the left, supporting evidence records on the right. 06Scientific AI copilot
A grounded conversational layer over the entire graph. Ask in plain language; receive an answer with citations to the specific records that support it.
- Retrieval-grounded - answers are constructed from indexed evidence, and every claim is linked
- Refuses to answer beyond the evidence rather than filling gaps with plausible text
- Full session history, shareable with colleagues
- Works across the whole corpus: literature, trials, patents, bioactivity, structures
Asset Discovery · Grounded copilotA multi-part question answered with inline citations and an expandable source list. 07Evidence, patent and trial intelligence
The reference layer the other six draw on - and the export that carries a finding out of the platform intact.
- Evidence Explorer - every record supporting a claim, filterable by source, date, study type and confidence
- Patent Intelligence - estate mapping, expiry timelines, freedom-to-operate signals
- Trial Intelligence - landscape, status transitions, sponsor behaviour, endpoint patterns
- Asset Dossier - a single export-ready document per asset, assembled automatically
Asset Discovery · Patent estate timelineExpiry and exclusivity horizon. Asset Discovery · Asset dossierGenerated dossier, ready for export.
Shelved & white space
The interesting cell is the one nobody is in
Strong biological support and no active competitive programme. Hover or focus any cell to read it. This grid is a schematic of the view - the live matrix is drawn from the graph, at the scale of your therapeutic area.
| IND-1 | IND-2 | IND-3 | IND-4 | IND-5 | IND-6 | IND-7 | IND-8 | |
|---|---|---|---|---|---|---|---|---|
| TGT-A | TGT-A × IND-1 Evidence: strong support Competition: no active programme White space | TGT-A × IND-2 Evidence: moderate support Competition: one programme | TGT-A × IND-3 Evidence: weak support Competition: crowded | TGT-A × IND-4 Evidence: no support Competition: several programmes | TGT-A × IND-5 Evidence: strong support Competition: crowded | TGT-A × IND-6 Evidence: weak support Competition: no active programme | TGT-A × IND-7 Evidence: moderate support Competition: several programmes | TGT-A × IND-8 Evidence: no support Competition: no active programme |
| TGT-B | TGT-B × IND-1 Evidence: weak support Competition: one programme | TGT-B × IND-2 Evidence: strong support Competition: no active programme White space | TGT-B × IND-3 Evidence: moderate support Competition: several programmes | TGT-B × IND-4 Evidence: strong support Competition: crowded | TGT-B × IND-5 Evidence: no support Competition: one programme | TGT-B × IND-6 Evidence: moderate support Competition: no active programme | TGT-B × IND-7 Evidence: weak support Competition: several programmes | TGT-B × IND-8 Evidence: weak support Competition: one programme |
| TGT-C | TGT-C × IND-1 Evidence: moderate support Competition: crowded | TGT-C × IND-2 Evidence: weak support Competition: several programmes | TGT-C × IND-3 Evidence: strong support Competition: one programme | TGT-C × IND-4 Evidence: weak support Competition: no active programme | TGT-C × IND-5 Evidence: moderate support Competition: one programme | TGT-C × IND-6 Evidence: strong support Competition: no active programme White space | TGT-C × IND-7 Evidence: no support Competition: no active programme | TGT-C × IND-8 Evidence: moderate support Competition: several programmes |
| TGT-D | TGT-D × IND-1 Evidence: no support Competition: no active programme | TGT-D × IND-2 Evidence: moderate support Competition: several programmes | TGT-D × IND-3 Evidence: weak support Competition: one programme | TGT-D × IND-4 Evidence: moderate support Competition: crowded | TGT-D × IND-5 Evidence: strong support Competition: several programmes | TGT-D × IND-6 Evidence: weak support Competition: crowded | TGT-D × IND-7 Evidence: strong support Competition: no active programme White space | TGT-D × IND-8 Evidence: weak support Competition: no active programme |
| TGT-E | TGT-E × IND-1 Evidence: strong support Competition: several programmes | TGT-E × IND-2 Evidence: no support Competition: one programme | TGT-E × IND-3 Evidence: moderate support Competition: no active programme | TGT-E × IND-4 Evidence: weak support Competition: several programmes | TGT-E × IND-5 Evidence: weak support Competition: one programme | TGT-E × IND-6 Evidence: moderate support Competition: crowded | TGT-E × IND-7 Evidence: moderate support Competition: one programme | TGT-E × IND-8 Evidence: strong support Competition: no active programme White space |
| TGT-F | TGT-F × IND-1 Evidence: weak support Competition: no active programme | TGT-F × IND-2 Evidence: moderate support Competition: one programme | TGT-F × IND-3 Evidence: no support Competition: several programmes | TGT-F × IND-4 Evidence: strong support Competition: no active programme White space | TGT-F × IND-5 Evidence: moderate support Competition: several programmes | TGT-F × IND-6 Evidence: no support Competition: no active programme | TGT-F × IND-7 Evidence: weak support Competition: crowded | TGT-F × IND-8 Evidence: moderate support Competition: one programme |
- Evidence
- Competition
- none crowded
Shelved, not failed
A programme that stopped for portfolio reasons is a different object from one that stopped on a safety signal, and the difference is visible in the record. The detector reads trial-registry state changes, publication silence, patent maintenance behaviour and corporate disclosure patterns together, and reports which of them fired.
- Trial-registry state changes
- Publication silence after an active period
- Patent maintenance behaviour
- Corporate disclosure patterns
Data and AI
What the platform reads, and what reasons over it
| Domain | Sources |
|---|---|
| Literature | PubMed / MEDLINE abstracts and metadata |
| Clinical | ClinicalTrials.gov registry, status history and results postings |
| Bioactivity & chemistry | ChEMBL, PubChem, DrugBank |
| Targets & proteins | UniProt, Open Targets, Reactome, DisGeNET |
| Structures | Protein Data Bank, AlphaFold structure predictions |
| Regulatory & IP | FDA Orange Book, SureChEMBL patent chemistry |
Ingestion runs continuously. Source, version and retrieval date are recorded for every record, so any result can be reproduced against the state of the evidence at the time it was generated.
Models
Graph neural networks
Link prediction across the target–disease–compound graph.
Biomedical transformer language models
Entity recognition, relation extraction and normalisation across free text.
Gradient-boosted ensembles
Tractability, druggability and prioritisation scoring.
Retrieval-augmented LLMs
The copilot layer and dossier generation, constrained to indexed evidence with mandatory citation.
Autonomous agents
Standing discovery briefs, run on a schedule, reporting only what changed.
Every model output carries a confidence estimate and the inputs that produced it. Nothing is presented as a conclusion when it is a prediction.
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