Discover therapies for rare cancers at the speed of thought
An AI research agent for oncology. Search literature, design molecules, predict drug properties, plan trials, and build complete research strategies. All from a single question.
Why this matters
Rare cancers account for 27% of all cancer diagnoses. Less than 5% of clinical trials target them.
Each rare cancer type has fewer than 200,000 patients, too small for traditional pharma ROI models. The research that does exist is scattered across incompatible databases, siloed registries, and paywalled journals.
What Zeta does
Connects fragmented knowledge
Rare cancer databases, clinical trial registries, PubMed literature, drug labels, and molecular data, queried together through a single research agent instead of 9 separate interfaces.
Reasons across sources
Zeta follows leads. A biomarker found in one database triggers drug lookups in another, cross-references active clinical trials, and surfaces related literature automatically.
Built on 12+ years of rare cancer drug development
The curated knowledge base behind Zeta comes from Lantern Pharma's work advancing therapies for cancers that traditional pharma economics overlook. Built for the edge cases.
The world's deepest rare cancer knowledge
Four specialized databases, unified into a single ontology connecting genes, drugs, trials, and phenotypes
Rare Cancers Knowledge Base
Curated database linking rare cancer types to therapeutic compounds, biomarkers, and clinical research
Cancer Drug Database
Comprehensive cancer drug database with FDA approval status, clinical trial data, and molecular targets
Clinical Trials Database
Self-hosted index of all registered clinical studies from ClinicalTrials.gov via AACT
Rare Cancers Library
Comprehensive document library with research papers, clinical guidelines, and treatment protocols
Authoritative medical databases and regulatory sources
Every query can pull from any combination of these sources in real time
PubMed / PMC
Real-time literature search across PubMed and PubMed Central
ORPHANET
European rare disease ontology with expert-curated information
NCI Thesaurus
Standardized cancer terminology ontology from the National Cancer Institute
OpenFDA Drug Labels
Human Phenotype Ontology
Ontology Lookup Service
Molecular Features
SMILES Validation
ChEMBL & PubChem
CellosaurusAll sources queried in real time. No stale caches or pre-indexed snapshots.
Predict drug penetration. Design novel compounds.
Purpose-built AI models for molecular analysis and blood-brain barrier prediction.
AI-Powered Chemical Reasoning
Advanced AI reasoning model that thinks through complex chemistry problems step-by-step. ETHER0 explores drug design opportunities by generating hypotheses, evaluating molecular structures, and proposing novel therapeutic candidates. It identifies promising scaffolds and optimizes drug-like properties for rare cancer targets.
Read about ETHER0
Blood-Brain Barrier Prediction
Industry-leading 16-model ML ensemble predicting blood-brain barrier permeability. Trained on 7,000+ compounds for targeted therapy development in CNS rare cancers. Identifies which treatments can effectively reach brain tumors and neurological targets.
Visit PredictBBB.aiAsk a question. Get a complete investigation.
Zeta investigates autonomously across multiple rounds of research, deciding when to dive deeper and when to synthesize.
Traditional Research
Manual Literature Mining
Search PubMed for rare cancer papers, sift through abstracts one by one, no connections between entities
3-4 hoursClinical Trial Hunting
Navigate ClinicalTrials.gov filters, decode eligibility criteria, manually match patient biomarkers
2-3 hoursDrug-Target Mapping
Cross-reference FDA labels, ORPHANET disease data, and molecular databases separately
2-3 hoursEvidence Synthesis
Compile fragmented findings from disparate sources, manually identify therapeutic opportunities
2-4 hoursManual, fragmented, time-intensive
Research with Zeta
Natural Language Query
Ask your research question in plain English about rare cancer therapies, biomarkers, or trials
InstantAutonomous Multi-Source Search
Zeta simultaneously queries rare cancer databases, clinical trials, literature, and drug data
SecondsRecursive Investigation
AI analyzes initial findings, decides which leads warrant deeper investigation, executes follow-up searches
MinutesSynthesized Insights
Citation-backed analysis with drug candidates, trial matches, biomarker correlations, and knowledge graphs
CompleteAutonomous, integrated, accelerated
See Zeta in action
From question to insight in minutes. Watch how Zeta transforms rare cancer research with autonomous intelligence.
Start your next investigation in 30 seconds
Stop wasting weeks searching through fragmented databases and disconnected literature. Zeta's recursive AI agents work 24/7 to synthesize evidence, identify novel drug combinations, and surface research-grade synthesis you'd never find manually.