Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Topological and Geometric Data Analysis
Abstract
This paper explores the application of Geometric Information Theory (GIT) to computational biology, aiming to develop a novel measure of 'information content' within biological systems. GIT leverages the geometric structure of molecules and cellular processes to quantify complexity and connectivity, offering a potentially more insightful approach to understanding biological function. We propose a new metric, the 'Geometric Complexity Index' (GCI), that directly reflects the intricate interplay of molecular geometry and process dynamics. The paper details the theoretical foundation of GIT, outlines the implementation of the GCI, and discusses its potential implications for various computational biology applications, including protein folding, gene expression analysis, and drug design. The core claim is that GIT provides a more robust and nuanced measure of information content compared to traditional information theory approaches, particularly when considering the complex, geometrical nature of biological systems.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
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