How Omic Works
A technical deep-dive into our AI-driven drug discovery platform: from multi-omics data to clinical candidates
Omic combines autonomous AI scientists with mechanistic digital patient models to discover drugs at unprecedented speed and accuracy. Our platform doesn't just predict—it reasons, hypothesizes, experiments, and learns.
Platform Architecture
The Discovery Process
Multi-Omics Data Integration
We ingest and harmonize data across all biological layers: DNA sequences and variants (genomics), gene expression patterns (transcriptomics), protein abundance and modifications (proteomics), and metabolite profiles (metabolomics).
Digital Patient Construction
We build mechanistic models that simulate patient biology at the molecular level. Unlike black-box predictions, our digital patients capture the actual causal relationships between genes, proteins, pathways, and disease phenotypes.
Autonomous AI Target Discovery
Our AI scientists operate autonomously—forming hypotheses, designing computational experiments, analyzing results, and iterating. They work 24/7, exploring hypothesis spaces no human team could cover.
- •Read and synthesize scientific literature in real-time
- •Generate and test mechanistic hypotheses about disease
- •Write custom analysis code and ML models
- •Identify and validate druggable targets
Generative Molecule Design
Once targets are validated, we generate novel small molecules optimized for potency, selectivity, and drug-like properties. Our generative models explore chemical space efficiently, producing candidates with favorable ADMET profiles.
Virtual Clinical Simulation
Before any molecule is synthesized, we simulate its effects across our digital patient populations. This predicts efficacy, identifies responder subgroups, optimizes dosing, and flags potential safety concerns.
Clinical Candidate Nomination
Candidates that pass all computational validation proceed to wet lab confirmation and clinical development, with comprehensive dossiers including predicted therapeutic windows, patient stratification criteria, and biomarker profiles.
View our current pipeline →Platform Performance
Frequently Asked Questions
How does Omic's AI drug discovery work?
Omic's platform integrates multi-omics data to build digital patient models. Autonomous AI scientists analyze these models to discover drug targets, design novel molecules, and simulate clinical outcomes—all computationally before wet lab work begins.
How accurate is Omic's AI?
Omic achieves 93.3% accuracy on the GPQA Diamond benchmark—the highest score of any AI system on PhD-level science questions. Our digital patient models demonstrate strong predictive accuracy for drug response across multiple therapeutic areas.
What makes Omic different from other AI drug discovery companies?
Omic uniquely combines: (1) Autonomous AI scientists that independently form and test hypotheses; (2) Mechanistic digital patient models built from multi-omics data; (3) End-to-end discovery from target identification through virtual clinical trials.
How long does AI drug discovery take with Omic?
Omic identifies validated drug targets in weeks rather than years. The complete discovery cycle from target identification to clinical candidate nomination typically takes 3-6 months, compared to 3-5 years for traditional approaches.
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Partner with Omic to discover drugs faster, with higher success rates.
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