Background
In 1986, Don R. Swanson, an information scientist, discovered that medical literature is siloed — two fields may both publish about a shared biological mechanism (A) without ever citing each other. If disease B is linked to A, and compound C also affects A, then C may treat B — even though no paper has ever studied them together. This tool applies that insight at scale across curated biomedical databases to surface genuine leads that could accelerate drug discovery.
Applications
- –Drug repurposing — approved compounds that may treat diseases never tested against
- –Target discovery — shared biological mechanisms between a disease and a potential therapy
- –Hypothesis generation — candidates worth investigating in the lab or clinic
- –Literature gaps — connections that exist in data but haven't been studied together
Data Sources
- B↔ACTDComparative Toxicogenomics Database — manually curated disease–gene associations backed by direct experimental evidence., Open TargetsGenetic, somatic, and genomic evidence linking diseases to target genes from the Open Targets Platform., DISEASESJensen Lab — curated and high-confidence text-mined disease–gene associations. CC BY 4.0. — disease–gene associations
- C↔ACTDComparative Toxicogenomics Database — human curated chemical–gene interactions., DGIdbDrug–Gene Interaction Database — aggregates interactions from DrugBank, PharmGKB, ChEMBL, and ~30 other sources., DrugCentralClinically active drug–target pairs curated from FDA labels, WHO essential medicines, and biomedical literature. — drug–gene interactions
- B↔CPubMedLive search against PubMed — checks whether a direct disease–drug connection already exists in the published literature. — checks if a connection already exists in print
How to Use
- 01Filter by column — click any column label to open a text filter. Type a term, press Enter or comma.
- 02Sort — click the arrow next to any column label. Click the score label to switch metric.
- 03Source filter — click the Sources column header to filter by supporting database.
- 04Rx only — toggle to show only hypotheses where the compound (C) is an FDA-approved drug.
- 05Star & export — click ☆ on any row, then Export CSV to download picks.
Scoring
- Novelty— how undiscovered the drug–disease connection is, based on existing PubMed literature (B–C papers)
- Strength— how well-supported the bridge mechanism is; both sides of the triangle must have evidence
- Combined— equal-weighted average; highest when unexplored and well-evidenced through a bridge