The Omics Co-processor for
AI-Driven Drug Discovery
Plug OmniTx into your AIDD platform to add multi-omics reasoning and mechanistic intelligence—without replacing your stack.
OmniTx
Explainable
Hypotheses
Built at World-Class Institutions
The Multi-Omics Advantage
Higher success rate for genetically supported targets
Drug targets with genetic support are 2Ă— more likely to succeed in clinical development.
Of omics data remains siloed
Most multi-omics datasets are never integrated—leaving critical biological context undiscovered.
Saved per target validation cycle
Integrated multi-omics reasoning can compress months of manual curation into weeks.
A Reasoning Layer, Not a Replacement
OmniTx complements your existing platforms with specialized biomedical intelligence
What OmniTx Delivers
An API-first multi-omics knowledge graph and reasoning engine designed to act as a specialized co-processor for your AI-driven discovery stack.
Curated Knowledge Graph
Continuously evolving multi-omics data integrating transcriptomics, genomics, proteomics, and clinical signals
Multi-Hop Reasoning
Generate, prioritize, and explain biological hypotheses grounded in mechanistic understanding
Interpretable Results
Structured hypotheses backed by explicit evidence chains with full provenance across omics and clinical sources
API-First Integration
Plugs into your existing platforms without requiring platform replacement
Multi-omics Knowledge Graph
Integrating diverse biological data for intelligent reasoning
Purpose-Built for Biomedical Reasoning
Three technical pillars differentiate OmniTx from generic AI models
Omics-Driven Knowledge Graph
A curated, harmonized multi-omics knowledge graph of genes, proteins, pathways, drugs, and clinical signals—designed for graph-augmented retrieval and multi-hop reasoning across biological pathways.
Multi-Agent Hypothesis Validation
Hypotheses undergo specialized validation through evidence retrieval, statistical checks, and causal plausibility assessments before presentation—improving robustness and biological plausibility.
Graph-Native AI Models
Link prediction and heterogeneous graph neural networks applied directly on the multi-omics graph to highlight under-explored relations and opportunities, even in data-sparse settings like rare diseases.
Tangible Value Across Your R&D Pipeline
Accelerate Discovery
Shorten the time from heterogeneous datasets to a ranked, explainable list of hypotheses for any disease or pathway.
Increase Confidence
De-risk multi-million dollar go/no-go decisions by integrating multi-omics, pharmacology, and clinical signals into a single coherent evidence layer.
Reduce Duplication
Surface where mechanisms and targets are well-explored versus under-explored, focusing resources on novel biology.
Build Reusable Assets
Create a dynamic knowledge graph that accumulates evidence and improves with every dataset and project outcome fed back into it.
AI-Driven Drug Discovery Platforms
We partner with AI-first organizations building the future of drug discovery
AI-First Drug Discovery Platforms
The Challenge
Data integration bottlenecks, shallow biology graphs, and the high cost of building comprehensive knowledge graphs from scratch. Existing platforms excel at chemistry and structure prediction but need deeper mechanistic biology to validate targets and understand disease pathways.
How We Help
Provide high-resolution, curated multi-omics knowledge graphs and mechanistic hypotheses that integrate seamlessly into your target discovery and chemistry engines. OmniTx acts as your specialized biology reasoning layer—complementing your existing AI stack with deep pathway understanding and evidence-backed target validation.
Our Phased Engagement Model
We use a risk-minimized, trust-building approach to partnership
Discovery Sprint
8-12 week scoped project on one disease area. Demonstrate value through preliminary knowledge graph and top N hypotheses.
Co-Development
Deep integration on 1-2 disease areas with iterative cycles. Partner gets exclusive asset rights; OmniTx keeps platform IP.
Strategic Partnership
Multiple programs under one licensing framework with milestone payments and royalties. Repeatable, scalable collaboration model.
Built by Experts in AI & Multi-Omics
Track record in production AI systems and applied biomedical research
Professor Fatemeh Vafaee
Scientific Leadership
Deputy Director (Science), UNSW AI Institute
Led national and international programs in AI-driven drug discovery and precision medicine. 80+ publications, $20M+ in R&D funding, and 30+ AI-enabled products from proof-of-concept to market deployment. Globally recognized with awards in AI (Health) and Bioinformatics.
Previously developed an AI-driven drug combination discovery platform, AI-enabled blood test for early breast cancer detection, and multiple multi-omics biomarker discovery programs with industry partners.
Arash Atashnama
Commercial & Partnership Leadership
Co-founder & Director | Former CTO, FOS Medical
Serial entrepreneur in MedTech and biotech with background in venture capital, AI-enabled target discovery, and AI medical imaging. Two-time founder with expertise in commercializing AI applications for healthcare.
Leads commercial strategy, partnerships, and early-stage company building. Active networker in the pharma/AI ecosystem with attendance at major conferences including EANM and engagement with the Australian biotech community.
Become a Design Partner
We're seeking design partners in oncology and related therapeutic areas who want to evaluate an embedded omics reasoning layer for their AI discovery stack.
Early Access
Get first access to new capabilities and features
Preferential Terms
Exclusive commercial terms for design partners
Roadmap Influence
Shape the product roadmap and feature development
Investment Opportunity
Potential to invest and form joint ventures
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