Every candidate explains why it ranked.
90% of drug candidates fail in clinical trials. Target identification still relies on manual literature review — a process that cannot systematically integrate genomics, proteomics, and clinical outcomes simultaneously. Skippy combines 11 peer-reviewed scoring methods across 74.7M verified findings, with 10 explainability overlays that show the evidence path from drug to mechanism to disease.
What a response looks like
Representative API output across three core use cases — repurposing, deep pair analysis, and combination scoring.
AMPK activation → mTOR suppression → autophagy enhancement; neuroprotective in multiple animal models
mTOR inhibition → reduced tau hyperphosphorylation and amyloid-β production
Glutamate release inhibition → reduced excitotoxicity in hippocampal neurons
PPARγ agonism → reduced neuroinflammation via microglia modulation
GSK-3β inhibition → reduced tau phosphorylation; established neuroprotective evidence
Simulated output representative of real API responses. 11 scoring methods, 50+ sources, temporal holdout benchmark. Research use only.
Six capabilities. One API.
Drug repurposing
Enter a disease; receive ranked drug candidates combined across 11 scoring methods — Swanson ABC path score, DWPC hub-corrected, genetic evidence, signature reversion, RWR PageRank, network proximity, and more.
Pair analysis
Deep analysis of a specific drug–disease pair: ABC path count and p-value, network proximity, genetic evidence (OMIM, GWAS, DisGeNET), L1000 signature reversion correlation, novelty score, and key intermediary genes.
Combination scoring
Score drug pairs by target complementarity and Jaccard overlap. Low Jaccard over shared intermediary genes = orthogonal mechanisms = combination potential. Cross-validated against NCI ALMANAC synergy data.
Genetic evidence
GWAS Catalog, OMIM, DisGeNET, and ClinVar evidence for a gene–disease association, returned as structured confidence scores. Used as a scoring component and available as a standalone lookup.
ABC pathway explorer
Enumerate Swanson ABC paths between a drug and disease through shared intermediary genes. Returns path count, top intermediaries, and hypergeometric p-value for shared-gene significance.
Batch pipeline
Run the full discovery pipeline across a list of diseases in parallel. Export results as structured JSON or CSV — useful for pipeline screens, indication expansion, and systematic portfolio analysis.
11 scoring methods. One composite.
Composite = weighted sum of 11 peer-reviewed methods × (1 − ABC p-value). Weights are configurable via the Hypothesis Workbench (M9). Temporal holdout benchmark — not random splits, which inflate AUC by 5–15%.
Validated on 35 curated known drug-disease pairs using temporal holdout — training on evidence before a cutoff date, validating on pairs discovered after. Random splits inflate AUC by 5–15%; temporal holdout does not. Recall@10, Recall@50, MRR, with 95% bootstrap confidence intervals.
Classic discoveries that anchor the benchmark
Swanson's 1986 fish oil → Raynaud's discovery is where ABC-path repurposing began. These known pairs validate the methodology — if the engine can't rediscover them, it shouldn't be trusted on unknowns.
Swanson's original discovery: omega-3 fatty acids connected via blood viscosity pathways.
AMPK activation → mTOR suppression → autophagy enhancement. Multiple ongoing clinical trials.
JAK inhibition attenuates cytokine storm while preserving antiviral response. FDA EUA granted.
mTOR inhibition links to multiple age-related diseases via autophagy and cellular senescence.
Most published drug repurposing systems report AUC on random train/test splits. Random splits allow drug-disease pairs from the same drug to appear in both train and test — inflating AUC by 5–15% without improving real-world discovery performance.
Skippy Drug Discovery uses temporal holdout: train on evidence before a cutoff date, validate on pairs first described after. Harder. Less impressive on paper. More informative about real performance.
“A ranked list without mechanism is a ranked list. Every Skippy candidate shows the intermediary genes, the methods that agree, the methods that disagree, and the evidence path from drug to disease.”
Skippy Drug Discovery · 10 explainability overlays per candidate
10 overlays. Every candidate.
Not post-hoc explanations bolted on after scoring — each overlay is a first-class output of the discovery pipeline, computed alongside the score.
SHA-256 tamper-evident pre-registration log. Timestamp predictions before outcomes are known — rigorous prospective validation.
Flag candidates where scoring methods diverge. High disagreement = lower confidence; surface it rather than hide it.
Score the biological plausibility of the drug-disease connection based on pathway coherence across the evidence base.
"What if this gene were not shared?" — identifies which intermediary nodes are load-bearing for the repurposing hypothesis.
Explains why a candidate ranks low: poor proximity, missing genetic evidence, or no signature reversion signal.
Flags candidates whose genetic evidence is derived predominantly from a single ancestry cohort — a known source of generalizability bias.
PubMed co-occurrence analysis: candidates with strong mechanistic signal but low prior publication overlap surface as high-novelty opportunities.
9 weight sliders let you co-author the scoring model live. Results update instantly — no server call — as you adjust method weights.
Jaccard-based target complementarity score for drug pairs. Low overlap over intermediary genes = orthogonal mechanisms = synergy candidate.
Pharma, biotech, and research teams
Discovery logged to tamper-evident registry
All discovery results are logged to a tamper-evident prediction registry with SHA-256 hash chain and versioned evidence base snapshots — enabling full traceability from discovery recommendation to any subsequent regulatory submission.
FAERS pharmacovigilance signals are integrated at the discovery stage to flag known safety concerns early. Every candidate surfaces with its evidence confidence and mechanism coherence score — not just a ranked list.
Often deployed together
FAERS signal detection integrated at the discovery stage. Before a repurposing candidate advances, Pharmacovigilance screens for known post-market safety signals.
Combination repurposing candidates need DDI screening. When two drugs share a CYP pathway, DDI surfaces the interaction before the combination is proposed.
Genetic evidence from GWAS and OMIM drives repurposing scores. Variants provides upstream ACMG/AMP interpretation for novel variants feeding the discovery pipeline.
Skippy Drug Discovery is a research tool for scientific hypothesis generation. It does not replace wet-lab validation, clinical judgment, or regulatory review. All repurposing candidates require independent experimental validation before any clinical or commercial application. Predictions are logged to a tamper-evident registry with timestamps — not guarantees of therapeutic efficacy.
See Drug Discovery in your pipeline
We work with pharma R&D, biotech, and academic research centers. Let's talk about your repurposing or target identification problem.