Deep EigenMatics Ranks #1 Globally in AI Drug Discovery, Outpacing All of Big Pharma in 2025 Patent Output
Advancing proprietary AI discovery of high-bioavailability Oral GLP-1 drug candidates.
HOUSTON, Feb. 3, 2026 /PRNewswire/ — Deep EigenMatics, Inc., a pioneer in high-velocity Artificial Intelligence for drug design, announced today that it has secured the #1 global ranking for new U.S. patent families in AI Drug Discovery methods for 2025.
In a landmark first year of operation, Deep EigenMatics has been issued four unique U.S. patent families. These foundational patents cover the company’s proprietary computational architectures for navigating chemical space. This volume of AI patents represents twice the combined output of the entire Big Pharma sector in this critical category. Deep EigenMatics is comparatively leaner and is delivering AI innovation at a velocity that Big Pharma R&D cannot match.
Leadership Perspective
Deep EigenMatics’ Founder & CEO, Dr. Stephen G. Odaibo—an AI engineer, practicing physician, mathematician, and biochemist—commented on the company’s breakthrough approach:
“Historically, drug discovery has been ad hoc. Our 2025 patent milestones protect the mathematical and biological methods that make discovery systematic. By fusing receptor biology with advanced AI, we are currently generating hundreds of thousands of novel candidates tailored for high bioavailability. We aren’t just generating drugs faster—our patented AI is selecting the drug candidates with the highest mathematical and biological probability of success in clinical trials.”
Innovation Velocity: A New Industry Standard
The company’s leadership is defined by two key metrics:
- Patent Family Volume: Deep EigenMatics leads all global entities in new AI-driven drug discovery methods IP for 2025, topping a list that includes all of Big Pharma, all of Big Tech, and all AI Discovery Startups.
- Innovation Velocity Index: Deep EigenMatics achieved an exceptional efficiency score, demonstrating that its breakthroughs are driven by elite, hyper-efficient teams—a sharp contrast to the resource-heavy, lower-yield models of legacy firms.
Strategic Target Announcement: Oral GLP-1
Building on this momentum, Deep EigenMatics is officially announcing the selection of the Oral GLP-1 category (Glucagon-like peptide-1 receptor agonists) as its lead therapeutic target.
“Our methods work smarter by selecting for bioavailability at the digital stage,” added Dr. Odaibo. “We are now applying our ‘Velocity Engine’ to the Oral GLP-1 generation. Our 2025 patents protect our discovery engine, and we anticipate the ‘Digital Lead Lock’ and subsequent composition of matter filings for our specific GLP-1 candidates in Q4 2026.”
Roadmap to 2027
Deep EigenMatics is moving immediately into the execution phase for its Oral GLP-1 candidate:
- March 2026: Initiation of massive-scale discovery compute.
- Q4 2026: Targeted “Digital Lead Lock” and filing of comprehensive WIPO patent families for lead targets.
- Q1 2027: Scheduled entry into formal pre-clinical testing.
Methodology & Data Integrity
The patent volume rankings and innovation velocity indices presented in this release were derived from an analysis of the United States Patent and Trademark Office (USPTO) Public Patent Search database for the 2025 calendar year. Data was deduplicated to unique patent families to ensure scientific rigor.
Technical Disclosure Note: The analysis utilized the following comprehensive Boolean search string targeting the intersection of advanced computational architectures and therapeutic discovery:
((AI OR AI/ML OR “Artificial Intelligence” OR “neural network” OR “Machine Learning” OR “Deep Learning” OR “Generative” OR Deep OR “Reinforcement Learning” OR “Autoencoder” OR “Diffusion Model” OR “Language Model” OR LLM OR Transformer).clm. AND (Drug OR “Drug Discovery” OR Protein OR Peptide OR “Amino Acid” OR Ligand OR “small molecule” OR compound OR antibody OR anti-body OR vaccine OR Binding OR mRNA).clm.) AND @pd >= “20250101” <= “20251231” AND (G16B* OR G06N*).cpc. AND (AI OR AI/ML OR “Artificial Intelligence” OR “neural network” OR “Machine Learning” OR “Deep Learning” OR “Generative” OR Deep OR “Reinforcement Learning” OR “Autoencoder” OR “Diffusion Model” OR “Language Model” OR LLM OR Transformer).ti,ab. AND (Drug OR “Drug Discovery” OR Protein OR Peptide OR “Amino Acid” OR Ligand OR “small molecule” OR compound OR “binding” OR antibody OR anti-body OR vaccine OR mRNA).ti,ab. AND ((Drug OR “Drug Discovery” OR Protein OR Peptide OR “Amino Acid” OR Ligand OR Pharma* OR “small molecule” OR compound OR antibody OR anti-body OR vaccine OR mRNA) NEAR15 (AI OR AI/ML OR “Artificial Intelligence” OR “neural network” OR “Machine Learning” OR “Deep Learning” OR “Generative” OR Deep OR “Reinforcement Learning” OR “Autoencoder” OR “Diffusion Model” OR “Language Model” OR LLM OR Transformer)).clm.
Exclusion Criteria: Following the initial query, the data was audited to exclude: (i) Continuations, Continuations-in-Part, and Divisional patent grants for which a family member (parent or sibling patent) had been previously granted, (ii) Patents with no functional relationship to AI drug discovery, and (iii) Patents with no direct link to drug discovery.
About Deep EigenMatics, Inc. Deep EigenMatics is an AI-first drug discovery and development company dedicated to accelerating the development of life-saving therapeutics through advanced proprietary AI techniques. Founded in February 2025, the company specializes in developing novel therapies for metabolic disorders, cancer, and rare diseases.
Strategic Growth & Inquiries Following its 2025 milestone as the global leader in AI-native US patent output in drug discovery methods, and its announcement of its lead target area (Oral GLP-1), Deep EigenMatics is evaluating strategic partnerships and institutional growth inquiries to accelerate its pipeline. Parties interested in the company’s Innovation Velocity Index and long-term roadmap may contact the Office of Strategic Initiatives.
Media Contact: [email protected]
Website: www.deepeigenmatics.ai
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SOURCE Deep EigenMatics
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