AI in Medicine
Authors: Michael Hetmann , Lena Parigger , Tobias Schopper , Markus Fleck , Georg Steinkellner , Christian C. Gruber
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.
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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). doi: https://doi.org/10.34749/3061-1466.2026.3