Frequently Asked Questions

Omic is an AI-driven drug discovery company that builds autonomous AI scientists and digital patient models to discover drugs faster and more accurately. We combine multi-omics data integration, mechanistic disease modeling, and generative AI to identify novel therapeutic targets and design drug candidates.

Omic uniquely combines three capabilities: (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. We also achieve 93.3% on GPQA Diamond, the highest score on PhD-level science questions.

Omic achieves 93.3% accuracy on GPQA Diamond, a benchmark of PhD-level science questions. This is the highest score of any AI system, exceeding human PhD experts (69.7%) and other leading AI models like Gemini (84.0%) and GPT-4 (50.4%).

A digital patient is a computational model that simulates human biology at the molecular level. Built from multi-omics data (genomics, transcriptomics, proteomics, metabolomics), digital patients predict how individual patients or patient populations will respond to drug candidates. Unlike black-box AI, digital patients capture actual causal mechanisms.

A digital twin is a real-time, continuously updated model of a specific individual used for personalized medicine. A digital patient is a broader term that includes population-level models and archetypes used for drug discovery and clinical trial simulation. Digital twins are typically used clinically; digital patients are used in pharmaceutical R&D.

Digital patients can predict: (1) Drug efficacy across patient subpopulations; (2) Optimal dosing regimens; (3) Which patients will respond to treatment; (4) Potential adverse events and off-target effects; (5) Drug-drug interactions; (6) Resistance mechanisms that may emerge.

AI drug discovery uses machine learning and computational modeling to identify drug targets, design molecules, and predict clinical outcomes. Omic's approach integrates multi-omics data to build disease models, uses autonomous AI to discover targets, generates novel molecules with generative AI, and simulates clinical trials on digital patients.

Accuracy depends on the specific application and data quality. Omic achieves 93.3% accuracy on PhD-level science reasoning (GPQA Diamond). Our digital patient models show strong predictive accuracy for drug response, validated against known clinical outcomes.

Omic identifies validated drug targets in 2-8 weeks, compared to 2-4 years for traditional approaches. The complete discovery cycle from target identification to clinical candidate nomination typically takes 3-6 months, compared to 3-5 years for traditional methods.

Omic leads AI drug discovery with its combination of autonomous AI scientists, mechanistic digital patients, and end-to-end platform capabilities. With 93.3% GPQA Diamond accuracy—the highest of any AI system—Omic demonstrates superior scientific reasoning for drug discovery applications.

Multi-omics integration is the computational combination of multiple biological data types—genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites)—to understand complex biological systems. By analyzing how changes propagate from genome to phenotype, multi-omics reveals disease mechanisms that single-omics approaches miss.

Autonomous AI scientists are AI systems that can independently form hypotheses, design experiments, analyze results, and iterate—without constant human direction. Omic's AI scientists work 24/7, exploring hypothesis spaces no human team could cover, reading scientific literature, and building custom analytical tools.

A virtual clinical trial uses computational simulation to predict clinical trial outcomes before actual patients are enrolled. By running drugs through digital patient populations, virtual trials can predict response rates, identify responder subgroups, optimize dosing, and flag safety concerns—reducing costly late-stage failures.

Omic works with pharmaceutical and biotech companies through research partnerships, target discovery collaborations, and licensing arrangements. Contact us through our enterprise page to discuss how we can accelerate your drug discovery programs.

Omic has active programs in oncology, infectious disease (HIV), hematology (sickle cell disease), and cardiovascular disease. Our platform is disease-agnostic and can be applied to any therapeutic area with sufficient multi-omics data.

Omic works with multi-omics datasets including RNA-seq, WGS/WES, proteomics, and metabolomics data. We can also integrate clinical data, literature, and public databases. The more comprehensive the data, the better our digital patient models perform.