Natalia SEGURA ALABART

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Interpretability and structural machine learning versus knowledge-base chemical property prediction

Predicting molecular properties such as solubility is a key challenge in computational chemistry and materials science. Machine learning offers a promising approach to model these properties efficiently, yet different paradigms exist for representing chemical information.

26-NOV-2025

Graph Neural Networks; Transfer Learning; Model Interpretability; Property Prediction

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