About withZeta

AI-Powered Research for Rare Cancer Drug Discovery

What is withZeta?

withZeta is an AI research platform designed to accelerate therapeutic development for rare cancers, which are diseases affecting fewer than 200,000 patients in the United States. These patients face a devastating reality: minimal treatment options not because their diseases are scientifically intractable, but because traditional pharmaceutical economics cannot justify the billion-dollar development costs for small patient populations. Zeta addresses this challenge by making comprehensive rare cancer research accessible to any investigator, anywhere, democratizing expertise and accelerating the path from biological understanding to therapeutic intervention.

The Rare Cancer Challenge

Drug development costs approximately $1-2 billion regardless of target population size. For common cancers, this investment can generate commercial returns that justify the expense and risk. For rare cancers with small patient populations, these identical development costs cannot be recouped through sales, making investment economically irrational from traditional pharmaceutical perspectives. This economic reality has created a systematic abandonment of hundreds of rare cancer patient populations.

The consequences are devastating. Cholangiocarcinoma, with approximately 8,000 US cases annually, sees patients facing median survival of just 12-18 months with minimal targeted therapy options despite clear molecular drivers. Adrenocortical carcinoma patients have virtually no effective systemic treatments beyond mitotane, which offers minimal response rates. Patients with MGMT-unmethylated glioblastoma face resistance to standard temozolomide chemotherapy with no additional approved options. Across 438 distinct rare cancer types characterized in Zeta's knowledge base, this pattern repeats: small populations, devastating prognoses, and minimal treatment options.

Agentic AI for Research

Zeta employs agentic AI, which refers to artificial intelligence systems that can accomplish complex goals with minimal supervision by making autonomous decisions and taking actions in dynamic environments. Unlike traditional AI that simply generates responses from training data, agentic systems actively pursue objectives by calling external tools, gathering information from multiple sources, and adapting their approach based on what they discover. In Zeta's architecture, specialized AI agents work together through orchestration: one agent might search PubMed for relevant literature while another queries clinical trials databases and a third analyzes molecular pathways, with their efforts coordinated to build comprehensive understanding. This multi-agent approach enables autonomous research workflows where the system decides which databases to search, what follow-up questions to investigate, and when sufficient evidence has been gathered, mirroring how expert researchers actually work rather than following rigid search protocols.

How Zeta Works

When you ask Zeta a research question, it follows a recursive investigation pattern that mirrors how scientists actually conduct research. The system enters a cycle: it analyzes what information is needed, calls relevant research tools (searching PubMed, querying clinical trials, analyzing molecular pathways), receives and interprets the results, thinks about what those findings mean and what gaps remain, then decides whether to investigate deeper or synthesize a final answer. If knowledge gaps persist, Zeta autonomously decides to recurse by calling new tools based on what it learned, going down research rabbit holes as needed, following leads across databases. Each recursion round builds on previous findings: early rounds might search broadly across diseases and treatments, middle rounds might dive deep into specific molecular mechanisms discovered, and later rounds might cross-reference clinical trial eligibility against identified biomarkers. The recursion continues until Zeta reaches an emergent base case, which is the moment when it determines that sufficient evidence has been gathered to answer your question comprehensively. This isn't a fixed number of searches; rather, the AI autonomously decides when investigation is complete based on evidence quality and coverage, just as an expert researcher knows when they've read enough papers to draw sound conclusions.

Zeta offers three research modes that you can switch between at any time based on your needs. Explorer mode provides fast, conversational research for iterative dialogue and quick insights, and is ideal for brainstorming, initial exploration, and targeted questions. Explorer uses efficient focused searches (up to 4 investigation rounds with 3 parallel tools) optimized for speed rather than comprehensive depth. Investigator mode conducts systematic, deep research across multiple evidence streams, using up to 10 investigation rounds with 6 parallel tools for comprehensive multi-database validation and evidence synthesis. Reporter mode synthesizes research conversations into structured, comprehensive reports and is best used after completing your investigation to transform dialogue into formal documentation. As Zeta investigates your question, you see its thought process in real-time: which databases it's searching, what evidence it's finding, why it's investigating deeper, and how it's synthesizing findings. This transparency helps you understand not just the answer, but how Zeta arrived at it, building trust and enabling you to evaluate the quality of the research.

Research Capabilities

Zeta integrates access to comprehensive medical databases and literature sources. It searches PubMed and MEDLINE, providing access to over 42 million biomedical citations and abstracts. Through ORPHANET, it accesses detailed information on more than 9,000 rare diseases with clinical descriptions and genetic data. The platform searches over 500,000 clinical trials from ClinicalTrials.gov, analyzing detailed protocols and eligibility criteria. It utilizes the NCI Thesaurus for cancer terminology, classifications, and relationships, as well as the Human Phenotype Ontology for disease-phenotype associations.

Beyond external databases, Zeta incorporates Lantern Pharma's proprietary Rare Cancer Knowledge Base, curated from published literature over more than a decade of focused research. This knowledge base characterizes 438 rare cancer types with detailed molecular profiles, catalogs 292 validated genetic biomarkers, documents 564 standard of care treatment protocols, links 532 therapeutic compounds to specific rare cancer indications, and includes 6,113 cancer cell line records with disease associations. This curated data enables Zeta to provide insights specific to rare cancers that might not be readily discoverable through general medical databases alone.

The power of Zeta's research capabilities rests fundamentally on meticulously curated databases that organize the fragmented landscape of rare cancer data into a unified ontology. Rare cancer research spans hundreds of distinct disease entities, each with unique molecular drivers, treatment responses, and clinical presentations scattered across disparate literature sources and disconnected databases. Lantern Pharma's decade-long curation effort has systematically structured this heterogeneous data by mapping synonymous disease names, linking molecular biomarkers to therapeutic responses, connecting cell line models to clinical indications, and building hierarchical relationships between cancer subtypes. This structured ontology serves as the semantic foundation upon which Zeta grounds its investigations, enabling the AI to reason about complex relationships, identify relevant evidence across naming variations, and synthesize insights that would remain invisible in unstructured data. Without this ontological framework, even the most sophisticated AI would struggle to navigate the terminological chaos and data fragmentation that characterizes rare cancer research.

For researchers developing therapies targeting the central nervous system, Zeta offers sophisticated molecular analysis capabilities. Its blood-brain barrier prediction system, powered by PredictBBB.ai, achieves 94.1% accuracy in evaluating whether drug candidates can penetrate the CNS, which is a critical consideration for treating brain cancers. The platform calculates comprehensive molecular properties and drug-likeness assessments, helping researchers optimize compounds before expensive synthesis and testing. For truly novel therapeutic development, Zeta integrates Ether0, a 24-billion parameter AI model capable of generating new molecular structures tailored to specific therapeutic requirements.

As you research with Zeta, the platform automatically constructs a living knowledge graph that evolves with every message in your conversation. This dynamic semantic network organizes your research dialogue into nodes (representing entities like diseases, drugs, genes, proteins, pathways, and clinical trials) and edges (representing relationships between them, such as "TREATS", "TARGETS", "ASSOCIATED_WITH", or "INHIBITS"). The graph builds incrementally: after each response, Zeta's extraction system identifies new entities mentioned in the conversation, links them to existing knowledge using a medical research ontology, and updates relationships based on new evidence discovered. Each node contains structured summaries and metadata, while edges capture specific facts with citation context and temporal validity. You can visualize this knowledge graph in an interactive 3D network view, search for specific entities, filter by type (genes, diseases, drugs), and export the complete graph structure for use in other tools. This transforms linear conversation into a navigable semantic map of your research domain, making it easy to spot patterns, trace causal chains, identify research gaps, and understand complex multi-entity relationships that would remain invisible in text-based research alone.

Who Uses Zeta

Zeta serves oncology researchers, drug discovery scientists, and clinical trial designers working to advance therapeutic options for rare cancer patients. Academic investigators use Zeta to accelerate systematic literature reviews that would traditionally take weeks to compile manually, synthesizing evidence across PubMed, clinical trials databases, and curated rare cancer knowledge in hours rather than days. Pharmaceutical R&D teams leverage the platform to evaluate rare cancer development opportunities, assessing biological plausibility and competitive landscapes before committing resources to expensive preclinical programs. Clinical trial designers use Zeta to identify appropriate patient populations and develop molecularly-informed eligibility criteria, while drug discovery scientists explore repurposing opportunities by identifying existing approved drugs that might address rare cancer indications through shared molecular mechanisms.

Common research workflows include rare cancer characterization (analyzing biomarkers, molecular drivers, and epidemiology for understudied diseases), drug repurposing discovery (identifying therapeutic candidates based on mechanism-of-action alignment), clinical trial matching (finding relevant trials for patients with specific molecular profiles), CNS drug optimization (evaluating blood-brain barrier penetration for brain cancer therapeutics), and combination therapy exploration (identifying synergistic treatment approaches through complementary mechanism analysis). Researchers also use Zeta to generate comprehensive knowledge graphs that visualize complex disease-drug-gene relationships, making it easier to identify research gaps and promising development pathways that might not be apparent from literature review alone.

Safety and Limitations

For detailed information about limitations and appropriate use, please review our Medical Disclaimerand Acceptable Use Policy.

About Lantern Pharma

Lantern Pharma is a clinical-stage biopharmaceutical company using AI to transform oncology drug development. The company's RADR® AI platform has validated over 2,000 drug-cancer matches through computational and experimental approaches, advancing five clinical programs targeting rare and difficult-to-treat cancers. With more than 12 years of AI-driven drug discovery experience, Lantern Pharma has published over 15 peer-reviewed papers on AI methodologies in oncology, contributing to the broader scientific understanding of computational approaches to therapeutic development.

Founded in 2013 and headquartered in Dallas, Texas, Lantern Pharma is publicly traded on NASDAQ under the ticker symbol LTRN. The company's mission focuses on addressing unmet medical needs in oncology through the strategic application of artificial intelligence and machine learning to drug discovery and development. For more information about Lantern Pharma's broader research portfolio and clinical programs, visit lanternpharma.com.

Beta Program

withZeta is currently in public beta, offering free access with beta credits for all registered users. Beta participants receive full access to all research tools and capabilities, including knowledge graph generation and export features. Community feedback is actively shaping development priorities and feature roadmap decisions. As a beta platform, users should expect occasional bugs, performance variability, and feature changes as we continue to refine and improve the system based on real-world usage patterns and researcher feedback. To request beta access, visit our homepage and sign up for an account.

Contact and Support

For general inquiries about withZeta, please contact us at contact@withzeta.ai. Technical support for platform issues is available at contact@withzeta.ai. If you identify potentially dangerous outputs, factual errors, or safety concerns, please report them immediately to contact@withzeta.ai so our team can investigate and take appropriate action. For inquiries about Lantern Pharma's research programs, clinical pipeline, or partnership opportunities, please visit lanternpharma.com/contact.

Additional Resources

To learn more about how we approach AI safety and responsible development, review our Responsible AI Policy. Our Terms of Service provide complete legal terms and beta program details. The Privacy Policyexplains how we handle and protect your data, including your rights regarding data access and deletion. For important information about platform limitations and safety considerations, consult our Medical Disclaimer. You can also explore Lantern Pharma's broader AI drug discovery work at lanternpharma.com/research.

Last Updated: January 17, 2026