Drug discovery with explainable artificial intelligence. - José Jiménez-Luna; Francesca Grisoni; Gisbert Schneider (Nat. Mach. Intell., 2020)

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment. - José Jiménez-Luna; Miha Skalic; Nils Weskamp; Gisbert Schneider (J. Chem. Inf. Model., 2021)

Structure-based drug discovery with deep learning. - Riza Özçelik; Derek van Tilborg; José Jiménez-Luna; Francesca Grisoni (CoRR, 2022)

Benchmarking Molecular Feature Attribution Methods with Activity Cliffs. - José Jiménez-Luna; Miha Skalic; Nils Weskamp (J. Chem. Inf. Model., 2022)

PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks. - Alejandro Varela-Rial; Iain Maryanow; Maciej Majewski; Stefan Doerr; Nikolai Schapin; José Jiménez-Luna; Gianni De Fabritiis (J. Chem. Inf. Model., 2022)

Explaining compound activity predictions with a substructure-aware loss for graph neural networks. - Kenza Amara; Raquel Rodríguez-Pérez; José Jiménez-Luna (J. Cheminformatics, 2023)

Improved motif-scaffolding with SE(3) flow matching. - Jason Yim; Andrew Campbell; Emile Mathieu; Andrew Y. K. Foong; Michael Gastegger; José Jiménez-Luna; Sarah Lewis; Victor Garcia Satorras; Bastiaan S. Veeling; Frank Noé; Regina Barzilay; Tommi S. Jaakkola (Trans. Mach. Learn. Res., 2024)