An on-demand multi-agentic co-scientist platform that supports the entire therapeutic development lifecycle — from early discovery through clinical and regulatory stages.
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Company (Nasdaq: LTRN)
The principles that guide every interaction with the platform and every partnership we form.
Transparent multi-agent collaboration that surfaces reasoning and evidence — not just answers — so your team stays fully in the loop.
A coordinated swarm of specialized scientific agents that think, critique, and iterate together — mirroring elite discovery and development teams, but faster and always available.
Every interaction, dataset, and outcome enriches the system, making the co-scientist progressively smarter with each engagement.
“This is the golden age of medicine.
We’re building the platform it deserves.”
A coordinated swarm of specialized agents that think, debate, and iterate the way elite drug-development teams do — only faster and always available.
Specialized agents for chemistry, biology, pharmacology, clinical strategy, and knowledge work in parallel and cross-critique one another for robust, defensible insights.
From target assessment and molecule design through translational strategy — one continuous, learning workflow that compresses timelines.
Built on Lantern Pharma’s proprietary oncology datasets, predictive models, and years of real-world R&D experience — now available on demand.
Instead of a single monolithic model, Open Medicine orchestrates a swarm of specialized agents that mirror the roles of a high-performing discovery and development team.
Designs and optimizes small-molecule candidates. Explores SAR, synthesizability, and property predictions in parallel with the rest of the swarm.
Maps disease biology, target pathways, and multi-omics signals. Surfaces the most actionable hypotheses from the underlying data fabric.
Models ADME/Tox, exposure, and safety early. Reduces late-stage attrition by bringing pharmacology into the design loop from day one.
Translates discovery into trial-ready thinking — biomarker strategy, patient selection, and regulatory-aligned development paths.
Continuously ingests literature, patents, proprietary datasets, and real-world evidence to keep the entire team current.
Balances scientific opportunity against competitive landscape, IP, and resource constraints to prioritize the highest-value paths.
Zeta is the AI Agent for rare cancer research and drug development, and is just one part of Open Medicine. Search literature, design molecules, predict drug properties, plan trials, and build complete research strategies. All from a single question.
Zeta provides detailed, citation-backed answers in seconds. Every claim is supported by research papers, clinical trials, or curated scientific literature. See the AI's reasoning process as it streams the response.
Get to know withZeta
Curated databases unified into a single ontology connecting genes, drugs, trials, and phenotypes — built on 12+ years of Lantern Pharma’s rare cancer drug development.
The central index linking rare malignancies to biomarkers, therapies, and cell lines — the primary curated source for disease characterization and treatment context.
A hierarchical classification system with 445 rare cancer leaf nodes — the normalization layer that relates cancer types across every other database.
A complete self-hosted mirror of ClinicalTrials.gov via AACT, searchable in natural language across every phase, condition, and intervention.
A curated therapeutic reference: mechanisms, molecular targets, indications, FDA labels, and safety data for approved and targeted therapies.
Full-text semantic search across curated papers, guidelines, and treatment protocols using hybrid vector and keyword matching with intelligent reranking.
FDA and EMA actions, approvals, orphan designations, trade press, and journals — resynced every six hours. The only source with knowledge of events after the model’s training cutoff.
Every query can pull from any combination of these in real time — no stale caches, no pre-indexed snapshots.
Real-time literature search across PubMed and PubMed Central with automatic MeSH term expansion and query optimization.
The European rare disease ontology — expert-curated prevalence, genetics, and clinical descriptions.
Standardized cancer terminology from the National Cancer Institute, used to reconcile naming across every other source.
Live access to official FDA labels: approved indications, contraindications, dosing, adverse events, and drug interactions.
Disease-to-symptom relationships and phenotypic abnormalities, mapped for cross-database rare disease characterization.
Cross-database entity linking and standardized vocabularies spanning the wider biomedical ontology landscape.
Authenticated cell lines from the Swiss Institute of Bioinformatics, with origin, disease associations, and links to ATCC, CCLE, and ChEMBL.
An autonomous Computational Biologist that designs experiments, builds rigorous cohorts, and delivers defensible results — purpose-built for the rare and pediatric cancers that have long been underserved, and powerful across every cancer type.
Most agentic tools wrap a language model around off-the-shelf software and will happily run a confounded cohort or the wrong statistical test, handing back a confident answer only an expert would recognize as wrong. ZetaOmics embeds domain judgment in all fourteen tools: when an analysis is flawed, it recognizes the flaw, declines to run it, and explains how to fix the experimental design.
Ask in plain English and it routes only to the datasets holding the modality you need — then resolves genome to proteome in one query across Ensembl, UniProt, OMIM, and Orphanet.
Profiles cohorts for hidden confounders and mixed pipelines before anything runs, then turns plain-language filters into analysis-ready groups with diagnostic feedback.
Auto-selects the right normalization and validates RNA against protein evidence. Consequence-aware stratification separates true loss-of-function from benign missense.
Adaptively picks edgeR, DESeq2, or limma-trend and refuses invalid comparisons, then runs enrichment across 228 gene set libraries.
Covariate-adjusted Cox modeling with hazard ratios and proportional-hazards diagnostics. Split cohorts by expression, signatures, metadata, or drug and CRISPR response.
Ranks druggable, tumor-restricted targets against the Human Protein Atlas — and pivots to plasma biomarkers when tissue data is sparse.
Describe a bespoke test in plain language and it generates and runs sandboxed code, then renders publication-quality figures that always match the numbers.
Stores, versions, and traces every result so multi-step workflows never rebuild — with a queryable audit trail, effect sizes, confidence intervals, power, and FDR enforced automatically.
Integrated computational chemistry for compound characterization, lead optimization, and de novo design — validated before anything downstream runs.
Roughly 87 physicochemical descriptors with six drug-likeness filters — Lipinski, Ghose, Veber, Rule of 3, REOS, and drug-like — each returned with a full scientific interpretation.
Chi connectivity and Kappa shape indices plus VSA descriptors that partition molecular surface by charge, lipophilicity, polarizability, and electronic state.
Validates chemical notation and generates 2D structure diagrams via RDKit, so every downstream prediction starts from a structure that actually parses.
Parallel compound lookups for identifiers, canonical SMILES, synonyms, bioactivity data, and molecular classification.
A 24B-parameter molecular reasoning model from FutureHouse: IUPAC-to-SMILES conversion, ADME optimization, retrosynthesis planning, reaction prediction, toxicity removal, and de novo generation — reasoning in English, answering in SMILES.
Many successful CNS drugs violate Lipinski’s rules, and many Lipinski-compliant drugs cannot cross the blood-brain barrier. Zeta reports the filters alongside the reasoning, so you can tell which is which.
A second Lantern Pharma platform, custom-built to work in tandem with Zeta. Fewer than 6% of small molecules cross the blood-brain barrier — knowing which ones do reshapes a CNS program from the very first decision.
Deep neural networks, random forest, SVM, and logistic regression, trained on 9,902 compounds from TDC and B3DB and validated against 1,316 unseen molecules.
Returns an ensemble probability, the prediction from every individual model, a confidence level derived from model agreement, and a plain-language clinical interpretation.
Identifies which candidates can reach brain tumors and neurological targets — and where structural modification is worth attempting before expensive synthesis.
Feature engineering from 4,939 RDKit candidates, full performance metrics, benchmark provenance, and documented limitations — all published openly.
Use code withzeta14 at signup to try withZeta™ Professional for 14 days free!
Join the waitlist for early access or explore how to invest and shape the future of AI-driven medicine.