AI in Medicine

CavitOmiX: A Proteome-Wide AI Framework for Structure-Based Off-Target Prediction, Drug Repurposing, and Antiviral Discovery

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Abstract

Polypharmacology — the unintended binding of drug candidates to non-target
proteins — remains a leading cause of late-stage clinical trial failure, yet it is
largely invisible to conventional sequence-based screening methods. We present
CavitOmiX, an AI-driven computational framework that addresses this problem
through proteome-wide cavitome analysis: systematic mapping of all potential
drug-binding sites across entire proteomes as high-dimensional physicochemical
point cloud fingerprints. Leveraging GPU-accelerated protein structure prediction
(AlphaFold2, ESMFold, OpenFold) and Innophore’s Catalophore™ fingerprinting
technology, CavitOmiX enables proteome-scale nearest-neighbour cavity searches.
The human cavitome, comprising over 800,000 binding site fingerprints derived
from 122,907 AI-predicted structural models (published open-source in Nature
Scientific Data), serves as a reference for early off-target liability prediction. The
same infrastructure supports drug repurposing by matching new targets against
approved drug binding sites, and antiviral lead optimization via a genetic algorithm.
We validate the framework across three use cases: resistance monitoring in SARS-
CoV-2, structural proteome analysis of Monkeypox virus, and full-pipeline antiviral
design for Chikungunya virus. All functionality is accessible via a public web
application at copilot.cavitomix.bio.

How to Cite:

Hetmann, M., Parigger, L., Schopper, T., Fleck, M., Steinkellner, G. & Gruber, C., (2026) “CavitOmiX: A Proteome-Wide AI Framework for Structure-Based Off-Target Prediction, Drug Repurposing, and Antiviral Discovery”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 17-20.