Case Studies
How we apply AI to drug discovery: from multi-omics data to small-molecule candidates
Targeting Myristoylation Across Cancer and Infection
Cancer & infectious disease
The Challenge
Certain cancers and infectious agents depend on protein N-myristoylation more heavily than healthy cells. Turning that shared dependency into a selective drug has been difficult.
Our Approach
Our AI scientists screened large chemical spaces against the pathway and used disease-specific digital patients to prioritize candidates predicted to spare healthy cells.
The Program
A small-molecule series engaging the myristoylation pathway, with potential to address both oncology and infectious-disease indications from a common chemical starting point.
Status
Advanced into lead optimization as a fully-owned program. The specific molecular target remains undisclosed.
Restoring Outflow to Lower Pressure in Glaucoma
Glaucoma
The Challenge
In the most common forms of glaucoma, fluid drains too slowly through the trabecular meshwork, raising intraocular pressure and damaging the optic nerve. Lowering pressure is the only proven way to slow progression.
Our Approach
Digital patient models helped prioritize small molecules acting on the outflow mechanism, balancing predicted efficacy against tolerability.
The Program
A small-molecule approach aimed at reducing trabecular-meshwork outflow resistance for durable intraocular-pressure reduction.
Status
Advanced into lead optimization as a fully-owned program. The specific target remains undisclosed.
A Trispecific Small Molecule for Solid Tumors
Solid tumors
The Challenge
Solid tumors often evade single-target immunotherapies by relying on redundant escape mechanisms. Engaging several targets at once may be required to overcome them.
Our Approach
Our AI searched for chemical matter capable of coordinated multi-target engagement, with digital patient models prioritizing candidates predicted to work across tumor subtypes.
The Program
A small-molecule trispecific designed to act on three targets simultaneously, combining the multi-target reach of biologics with the manufacturing and delivery advantages of small molecules.
Status
Advanced into lead optimization as a fully-owned program. The specific targets remain undisclosed.
Targeting GPR55 for Inflammatory Pain and Sickle Cell
Inflammatory pain & sickle-cell crisis
The Challenge
Inflammatory pain (including the vaso-occlusive crises of sickle cell disease) is poorly served by existing non-opioid options.
Our Approach
Our AI scientists designed and prioritized GPR55-targeting molecules on the platform, using digital patient models to predict response across patient backgrounds.
The Program
A small-molecule modulator of GPR55, a G-protein-coupled receptor implicated in inflammation and pain signaling.
Status
Advanced into lead optimization as a fully-owned program, optimizing potency, selectivity, and oral drug-like properties.
How We Achieve These Results
Multi-Omics Integration
Combine genomics, transcriptomics, proteomics, and metabolomics for complete disease understanding
Digital Patient Models
Mechanistic simulations predict drug response before synthesis
Autonomous AI Scientists
24/7 hypothesis generation, testing, and optimization
Frequently Asked Questions
How fast can Omic identify drug targets?
Omic's AI platform identifies validated drug targets in 2-8 weeks, compared to 2-4 years for traditional approaches. Our autonomous AI scientists analyze multi-omics data continuously to discover novel therapeutic opportunities.
What types of diseases has Omic worked on?
Omic has active small-molecule programs across oncology and infectious disease, ophthalmology, immuno-oncology, and inflammation. Our platform is disease-agnostic and can be applied to any therapeutic area with sufficient multi-omics data.
How does Omic validate its AI predictions?
We validate through computational cross-validation, comparison against known biology, and wet lab confirmation. Our digital patient models are continuously refined based on experimental feedback.
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