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bioRxiv · 10.64898/2026.01.06.697864

Graph-based Drug Decomposition for Anticancer Response Modeling

Nasini, S.·Armas, L. F. P.·Bouaggad, O.·Dabo, S.·Laurent, D.·Petit, A.·Cheok, M. H.
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Abstract

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.

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Nasini, S., Armas, L. F. P., Bouaggad, O., Dabo, S., Laurent, D., Petit, A., Cheok, M. H.. 2026-01-07. Graph-based Drug Decomposition for Anticancer Response Modeling. https://doi.org/10.64898/2026.01.06.697864

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