Development of a Modular Computational Virtual-Cell Framework Integrating Drug–Target Interactions, Intracellular Signaling, Energy Metabolism, and Vesicular Trafficking

Understanding drug effects at the cellular level requires consideration of interconnected processes, including molecular target engagement, intracellular signal transduction, energy metabolism, gene expression, and vesicular trafficking. Computational biology provides methods for representing these processes at different levels of detail, but integrating them in a transparent and extensible software architecture remains challenging [1–3]. This paper de scribes the conceptual design and initial Python implementation of a modular computational virtual-cell framework for a simplified eukaryotic cell. The framework uses a two-dimensional circular geometry, a discretized plasma membrane, membrane-associated components, mitochondria, microtubules, vesicles, and lysosomal compartments. The current implementation includes spatially proximity-based ligand–receptor interactions, receptor clustering, abounded second-messenger variable with an MAPK-associated proxy, simplified mitochondrial ATP accounting, delayedtranscription/translation events, and ATP-dependent vesicular transport. Drug–target interaction modeling and pathway-specific representations of RAS–RAF–MEK–ERK (MAPK/ERK), PI3K–AKT–mTOR, and JAK–STAT are specified as planned extensions; they are not yet complete molecular modules in the current code. Mathematical formulations are provided to guide these extensions and to make assumptions explicit. The framework is therefore presented as a developing computational architecture, not as an experimentally validated predictive model. Its purpose is to provide a modular foundation for subsequent implementation, software verification, parameterization, and biological validation.