| Prototype-based interpretable graph neural networks A Ragno, B La Rosa, R Capobianco IEEE Transactions on Artificial Intelligence 5 (4), 1486-1495, 2022 | 14 | 2022 |
| Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar. com portal E Proia, A Ragno, L Antonini, M Sabatino, M Mladenovič, R Capobianco, ... Journal of Computer-Aided Molecular Design 36 (7), 483-505, 2022 | 8 | 2022 |
| Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening M Proietti, A Ragno, BL Rosa, R Ragno, R Capobianco Machine Learning 113 (4), 2013-2044, 2024 | 7 | 2024 |
| Memory Replay For Continual Learning With Spiking Neural Networks M Proietti, A Ragno, R Capobianco 2023 IEEE 33rd International Workshop on Machine Learning for Signal …, 2023 | 5 | 2023 |
| Machine learning data augmentation as a tool to enhance quantitative composition–activity relationships of complex mixtures. A new application to dissect the role of main … A Ragno, A Baldisserotto, L Antonini, M Sabatino, F Sapienza, E Baldini, ... Molecules 26 (20), 6279, 2021 | 4 | 2021 |
| Semi-Supervised GCN for learning Molecular Structure-Activity Relationships A Ragno, D Savoia, R Capobianco arXiv preprint arXiv:2202.05704, 2022 | 1 | 2022 |
| Identifying Candidates for Protein-Protein Interaction: A Focus on NKp46’s Ligands A Borghini, F Di Valerio, A Ragno, R Capobianco | | 2024 |
| Transparent Explainable Logic Layers A Ragno, M Plantevit, C Robardet, R Capobianco ECAI 2024-27th European Conference on Artificial Intelligence, 19–24 October …, 2024 | | 2024 |
| Nuovi approcci metodologici applicati a Cencelle (Tarquinia, VT)-New methodological approaches applied to Cencelle (Tarquinia, VT) G Annoscia, R Astolfi, I Bellini, S Cavallero, C Chiovoloni, S D’Amelio, ... SPOLIA 20 (10 ns), 2024 | | 2024 |
| Understanding Deep RL Agent Decisions: a Novel Interpretable Approach with Trainable Prototypes. C Borzillo, A Ragno, R Capobianco XAI. it@ AI* IA, 1-14, 2023 | | 2023 |
| Oxidative DNA damage preventive activity of essential oils of three Pinus species: P. mugo, P. sibirica, and P. silvestre S Matić, T Mladenović, M Mladenović, N Tomašević, R CAPOBIANCO, ... | | 2023 |
| Molecule Generation from Input-Attributions over Graph Convolutional Networks D Savoia, A Ragno, R Capobianco arXiv preprint arXiv:2202.05703, 2022 | | 2022 |
| Py-Graph: An Easy-To-Use Interface for Building Graph-Based QSAR Models A Ragno, R Capobianco, R Ragno 2022, 2022 | | 2022 |
| Explainable AI in drug design: self-interpretable graph neural network for molecular property prediction using concept whitening M Proietti, A Ragno, R Capobianco 2022, 2022 | | 2022 |
| Explainable AI in Drug Design: Perturbation based molecular attributions using Graph Convolutional Networks A Ragno | | 2021 |
Roberto CapobiancoSapienza University of Rome - Sony AIVerified email at diag.uniroma1.it
