Graph-based Drug Decomposition for Anticancer Response Modeling

This paper studies the molecular effects on ex vivo drug response in pediatric acute myeloid leukemia (AML). We firstly estimate dose-response relationships through linear and mixed-effects models, capturing both patient-specific heterogeneity and drug-level effects. Then, drug identifiers are decomposed into curated molecular descriptors and their higher-order interactions, yielding a structured, interpretable representation of chemical properties. To handle the resulting high-dimensional system, we introduce a specialized graph-based drug decomposition and selection, enabling a computationally tractable estimation of the molecular effects on ex vivo drug response. This strategy uncovers the molecular features most strongly associated with cellular viability, providing a biologically grounded and transparent alternative to black-box predictive methods. By directly linking molecular structure to therapeutic outcomes, our framework supports a novel mathematical-programming-based drug-combination selection and cost-efficient compound prioritization in pre-clinical leukemia research.