@conference {70432,
	title = {MechGNN: A Graph Neural Network Framework for Learning Nanoparticle-Tumor Mechanical Interactions in Targeted Drug Delivery},
	booktitle = {2025 IEEE MIT Undergraduate Research Technology Conference (URTC)},
	year = {2026},
	month = {2026 May 26},
	abstract = {<p>Mechanical heterogeneity and collagen topology in tumor extracellular matrix (ECM) hinder nanoparticle (NP) transport and uptake, motivating models that couple NP mechanics with measured tumor mechanics. Non-invasive stiffness mapping (e.g., MR elastography) and histology-derived stiffness inference (STIFMap) enable learning on patientproximal mechanical maps. We introduce MechGNN, a dualgraph framework with cross-attention that represents NPs (core/shell/ligands) and ECM fiber networks as interacting graphs, and embeds physics-informed edge functions derived from Hertzian contact and receptor-ligand binding kinetics to regularize message passing. Using only public re-sources-STIFMap and open SHG collagen images quantified with CT-FIRE/CurveAlign/TWOMBLI-plus open FEM/BD simulations (FEniCS; Brownian dynamics) and open NP/ligand repositories (caNanoLab, BindingDB, IUPHAR/BPS Guide to Pharmacology), MechGNN achieves a\&nbsp;12-16\%reduction in penetration-depth RMSE relative to the strongest baseline on synthetic-from-real ECM testbeds and sustains an\&nbsp;8-13\%\&nbsp;RMSE gain in leave one tumor type out tests; physics losses reduce constraint violation rates by\&nbsp;≈35\%. The pipeline provides a reproducible bridge from measured ECM mechanics to mechanically designable NP properties.</p>
},
	keywords = {Contacts, drug delivery, Elasticity, Electronic countermeasures, Extracellular Matrix, Graph neural networks, Joining processes, Labeling, Modeling, nanoparticle mechanics, Physics, physics-informed learning, Printing, Textile fibers, Tumors},
	doi = {10.1109/URTC68753.2025.11533032},
	url = {https://doi.org/10.1109/urtc68753.2025.11533032},
	author = {Uppalapati, Khartik and Yimenicioglu, Bora and Abdulkareem, Shakeel and Eftekhari, Adan}
}
