Interpretability and structural machine learning versus knowledge-base chemical property prediction
Project detail
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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Assigned student
| Name | Surname | Assignment Date | Course |
|---|---|---|---|
| JEAN PHILIPPE | LEMOINE ROMERO | 01-SEP-2025 | 2025-2026 |
Files
| File | Document Type | Description |
|---|---|---|
| Memoria_LEMOINEROMERO_JEANPHILIPPE.pdf | Memòria |
Tribunal
lab231
17-JUN-2026 09:00
Pedro GARCÍA LÓPEZ
Francesc SERRATOSA I CASANELLES
Aïda VALLS MATEU