Deep EigenMatics offers the EigenMatics™ AI/ML platform that designs, ranks, and virtually screens hundreds of thousands of small‑molecule, peptide, and synthetic antibody candidates, delivering in silico predictions of ADMET, safety, and efficacy. Its cloud‑based dashboard and API integrate with existing R&D pipelines, allowing pharmaceutical companies, biotech firms, and CROs to accelerate lead identification across multiple therapeutic areas.
Funding
Funding not disclosed
Founders
Product
Problem
The discovery and optimization of small‑molecule and peptide therapeutics remain time‑intensive and resource‑heavy, limiting the number of candidates that can be evaluated for safety and efficacy before entering pre‑clinical or clinical stages. Traditional high‑throughput screening methods often lack the predictive power to prioritize compounds across diverse therapeutic areas, leading to high attrition rates.
Solution
Deep EigenMatics addresses these bottlenecks with its proprietary EigenMatics™ AI/ML platform, which can generate and virtually screen hundreds of thousands of small‑molecule, peptide, and synthetic antibody candidates in silico. The system leverages deep‑learning models trained on multi‑omics and pharmacological data to predict pharmacokinetic, safety, and efficacy profiles early in the design cycle. By iteratively optimizing molecular structures against target‑specific criteria, the platform accelerates lead identification and reduces the experimental workload required for pre‑clinical validation. The approach is applied across five therapeutic domains—cardiovascular‑renal‑metabolic, oncology, immunology, neurology, and ophthalmology—enabling parallel discovery programs. Results are delivered through secure cloud‑based dashboards that integrate with existing R&D data pipelines, facilitating rapid decision‑making for downstream development.
Target Audience
The primary customers are pharmaceutical companies, biotech firms, and contract research organizations seeking to accelerate early‑stage drug discovery and reduce attrition risk across multiple therapeutic indications. Academic research groups developing novel therapeutics also benefit from the platform’s high‑throughput design capabilities.
Features
- Proprietary deep‑learning engine that designs and ranks small‑molecule, peptide, and monoclonal synthetic biologic candidates at scale
- In silico safety and efficacy prediction models covering ADMET, off‑target effects, and disease‑specific biomarkers
- Automated generation of diverse antibody libraries targeting user‑defined antigens via the EigenMatics™ platform
- High‑throughput virtual screening of >100,000 compounds per project with iterative reinforcement‑learning optimization loops
- Cloud‑hosted analytics dashboard with API access for seamless integration into existing LIMS and data‑management systems
- Patent‑backed intellectual property framework supporting rapid IP generation and protection for novel designs
- Multi‑therapeutic area pipelines allowing simultaneous project execution across cardiovascular‑renal‑metabolic, oncology, immunology, neurology, and ophthalmology
- Compliance‑ready data handling with encryption and role‑based access controls for confidential drug‑discovery data