Ingenix provides a proprietary multimodal, multiscale generative AI co‑pilot that simulates clinical trials on digital twins of patients, integrating text, graphics, bio‑imaging, 3D structures, sequences, and longitudinal health records. The platform delivers rapid low‑level predictions—from molecular docking to cellular interactions—and aggregates them into detailed forecasts of clinical endpoints and adverse events, helping pharmaceutical and biotech teams design and prioritize trials with greater confidence.
Funding
$9.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
1OIVFounders
Product
Problem
Pharmaceutical companies face extremely high failure rates in clinical trials, with most candidate drugs not reaching regulatory approval, leading to massive financial losses and delayed patient access to new therapies. Existing AI tools provide only coarse, binary predictions and lack the ability to model the complex, multiscale biology that drives clinical outcomes.
Solution
Ingenix offers a proprietary multimodal, multiscale generative AI co‑pilot that simulates clinical trials on digital twins of patients. By ingesting and reasoning over text, graphics, bio‑imaging, 3D structures, sequences, and longitudinal health records, the model performs rapid low‑level predictions—from molecular docking to cellular interactions—and aggregates them into higher‑level insights about clinical endpoints and adverse events. Users query the system in natural language, receiving granular predictions of endpoint values, risk of outlier events, and explanations of the underlying biological drivers. This enables drug developers to evaluate first‑in‑class candidates, optimize trial design, and prioritize R&D investments with far greater confidence.
Target Audience
Primary customers are pre‑clinical development teams, clinical development groups, and strategic planning units within pharmaceutical and biotech companies that need detailed, biologically grounded forecasts of trial performance.
Features
- Multimodal foundation model that integrates genomics, transcriptomics, proteomics, imaging, and electronic health record data across molecular, cellular, tissue, organism, and population scales
- Chain‑of‑thought prompting interface allowing natural‑language queries and iterative exploration of prediction drivers
- Ultra‑fast molecular docking engine operating orders of magnitude faster than traditional tools, enabling proteome‑wide interaction simulations
- Granular prediction of clinical endpoint values and specific adverse events rather than simple success/failure outcomes
- Digital‑twin simulation of entire trial cohorts, supporting virtual trial design and scenario analysis
- Automated synthesis of insights into factors influencing outcomes, presented in interpretable reports for investigators