| A deep-learning approach toward rational molecular docking protocol selection J Jiménez-Luna, A Cuzzolin, G Bolcato, M Sturlese, S Moro Molecules 25 (11), 2487, 2020 | 19 | 2020 |
| Artificial intelligence in drug discovery: recent advances and future perspectives J Jiménez-Luna, F Grisoni, N Weskamp, G Schneider Expert opinion on drug discovery 16 (9), 949-959, 2021 | 208 | 2021 |
| Bayesian Optimization in Machine Learning J Jiménez Luna Universitat Politècnica de Catalunya, 2017 | | 2017 |
| Benchmarking molecular feature attribution methods with activity cliffs J Jiménez-Luna, M Skalic, N Weskamp Journal of Chemical Information and Modeling 62 (2), 274-283, 2022 | 25 | 2022 |
| Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment J Jimenez-Luna, M Skalic, N Weskamp, G Schneider Journal of chemical information and modeling 61 (3), 1083-1094, 2021 | 69 | 2021 |
| DeepSite: Protein binding site predictor using 3D-convolutional neural networks J Jiménez, S Doerr, G Martínez-Rosell, AS Rose, G De Fabritiis Bioinformatics 33 (19), 3036–3042, 2017 | 535 | 2017 |
| DeltaDelta neural networks for lead optimization of small molecule potency J Jiménez-Luna, L Pérez-Benito, G Martinez-Rosell, S Sciabola, R Torella, ... Chemical science 10 (47), 10911-10918, 2019 | 61 | 2019 |
| Drug discovery with explainable artificial intelligence J Jiménez-Luna, F Grisoni, G Schneider Nature Machine Intelligence 2 (10), 573-584, 2020 | 646 | 2020 |
| Explaining compound activity predictions with a substructure-aware loss for graph neural networks K Amara, R Rodríguez-Pérez, J Jiménez-Luna Journal of cheminformatics 15 (1), 67, 2023 | 4 | 2023 |
| Extracting medicinal chemistry intuition via preference machine learning OH Choung, R Vianello, M Segler, N Stiefl, J Jiménez-Luna Nature Communications 14 (1), 6651, 2023 | 15* | 2023 |
| Fast protein backbone generation with SE (3) flow matching J Yim, A Campbell, AYK Foong, M Gastegger, J Jiménez-Luna, S Lewis, ... arXiv preprint arXiv:2310.05297, 2023 | 21 | 2023 |
| Improved motif-scaffolding with SE (3) flow matching J Yim, A Campbell, E Mathieu, AYK Foong, M Gastegger, J Jiménez-Luna, ... arXiv preprint arXiv:2401.04082, 2024 | 3 | 2024 |
| KDEEP: Protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. J Jiménez, M Skalic, G Martinez-Rosell, G De Fabritiis Journal of chemical information and modeling 58 (2), 287–296, 2018 | 787 | 2018 |
| LigVoxel: inpainting binding pockets using 3D-convolutional neural networks M Skalic, A Varela-Rial, J Jiménez, G Martínez-Rosell, G De Fabritiis Bioinformatics 35 (2), 243-250, 2019 | 67 | 2019 |
| Machine learning in structural biology and chemoinformatics: Driving drug discovery one epoch at a time J Jiménez Luna Universitat Pompeu Fabra, 2019 | 1 | 2019 |
| Métodos Monte Carlo basados en cadenas de Markov J Jiménez Luna | 1 | 2015 |
| PathwayMap: molecular pathway association with self-normalizing neural networks J Jimenez, D Sabbadin, A Cuzzolin, G Martinez-Rosell, J Gora, ... Journal of chemical information and modeling 59 (3), 1172-1181, 2018 | 29 | 2018 |
| PlayMolecule BindScope: large scale CNN-based virtual screening on the web M Skalic, G Martínez-Rosell, J Jiménez, G De Fabritiis Bioinformatics 35 (7), 1237-1238, 2019 | 57 | 2019 |
| PlayMolecule glimpse: Understanding protein–ligand property predictions with interpretable neural networks A Varela-Rial, I Maryanow, M Majewski, S Doerr, N Schapin, ... Journal of chemical information and modeling 62 (2), 225-231, 2022 | 13 | 2022 |
| Predicting Protein-ligand Binding Affinities J Jiménez-Luna, G De Fabritiis | | 2020 |
Gianni De FabritiisComputational Science Laboratory, Icrea Research Professor, Acellera twitter:@gdefabritiis在 upf.edu 的电子邮件经过验证
