Modular multi-scale models for computational drug discovery

Virtual-biology models can struggle to capture dynamic interactions across biological scales and to generalize across species, cell types, donors, genotypes, microenvironments, doses, and timepoints. These limitations can make their predictions difficult to use in iterative drug-discovery workflows.

We are developing a modular framework designed to address these challenges. It will map disease- and treatment-relevant pathways, model their effects on cellular states and link those outputs to tissue and organ-level models. Fast surrogate models will support broad screening; more detailed simulations will be used when uncertainty or the consequences of a decision warrant them.

We will apply the approach to oncology and infectious-disease projects involving cross-species data. Our aim is to produce useful predictions across biological contexts while making the simulation unnecessarily complex.