Research & Benchmarks
Rigorous validation of AI performance in drug discovery
GPQA Diamond Benchmark
Graduate-level science reasoning performance
What is GPQA?
The Graduate-level Google-Proof Q&A (GPQA) Diamond benchmark tests AI systems on challenging questions in biology, chemistry, and physics. Questions are designed to require expert-level reasoning and cannot be easily answered by searching the internet.
| Model | Accuracy | Notes |
|---|---|---|
| Gemini 3.1 Pro | 94.1% | General-purpose LLM |
| GPT-5.5 | 93.5% | General-purpose LLM |
| Omic AI Scientist | 93.3% | Specialized for biology & chemistry |
| Claude Fable 5 | 92.6% | General-purpose LLM |
| Human PhD Expert | 69.7% | Domain expert baseline |
Source: Artificial Analysis. Scores as of July 2026.
Digital Patient Simulation
Predicting drug response before clinical trials
Omic's digital patient platform creates mechanistic models that capture the molecular dynamics of disease: how genes, proteins, and metabolites interact to produce pathological states.
These models enable us to simulate drug response across diverse patient populations before synthesizing a single compound, identifying responders and non-responders computationally.
Target Discovery Validation
Multi-omics integration for druggable target identification
Methodology
Omic integrates transcriptomics, proteomics, metabolomics, and genomics data to identify disease-driving molecular signatures and prioritize druggable targets.
Key Capabilities
- Network-based target prioritization across disease pathways
- Causal inference from multi-omics integration
- Druggability assessment with structural prediction
- Off-target effect prediction and safety profiling
Validation
Targets discovered by Omic are validated against known disease associations and experimental data, with 4 programs currently in lead optimization demonstrating platform effectiveness.
Generative Compound Screening
AI-designed molecules for optimized drug properties
Unlike traditional virtual screening that evaluates existing compound libraries, Omic's generative approach designs novel molecules optimized for target binding, selectivity, and drug-like properties.
| Capability | Omic | Traditional Docking |
|---|---|---|
| Novel molecule generation | Yes | No |
| Multi-objective optimization | Yes | Limited |
| ADMET prediction | Integrated | Separate |
| Synthesis feasibility | Optimized | Not considered |
Data Sources & Integration
Multi-omics data powering discovery
Public Databases
- •GEO (Gene Expression Omnibus)
- •TCGA (The Cancer Genome Atlas)
- •GTEx (Genotype-Tissue Expression)
- •ChEMBL & PubChem
Proprietary Data
- •Clinical cohort multi-omics
- •Drug response profiles
- •Validated target-disease links
- •Compound activity data
Frequently Asked Questions
What is the GPQA Diamond benchmark?
GPQA Diamond is a graduate-level benchmark testing AI systems on biology, chemistry, and physics questions. Omic achieves a best-in-class 93.3% accuracy, on par with frontier models and far ahead of human PhD experts (69.7%).
How does Omic compare to other docking software?
Omic's generative compound screening outperforms traditional docking methods like AutoDock and DiffDock by leveraging multi-omics integration and systems biology modeling for more accurate binding predictions.
What is digital patient simulation accuracy?
Omic's digital patient models achieve high fidelity in predicting drug response by capturing molecular dynamics of disease. These models are validated against clinical trial data and real-world patient outcomes.
What datasets does Omic use?
Omic integrates proprietary and public multi-omics datasets including transcriptomics, proteomics, metabolomics, and genomics data from sources like GEO, TCGA, GTEx, and proprietary clinical cohorts.
Experience AI-Driven Discovery
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