| 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 | 866 | 2018 |
| SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions JL Trincado, JC Entizne, G Hysenaj, B Singh, M Skalic, DJ Elliott, E Eyras Genome biology 19, 1-11, 2018 | 505 | 2018 |
| Shape-based generative modeling for de novo drug design M Skalic, J Jiménez, D Sabbadin, G De Fabritiis Journal of chemical information and modeling 59 (3), 1205-1214, 2019 | 230 | 2019 |
| From target to drug: generative modeling for the multimodal structure-based ligand design M Skalic, D Sabbadin, B Sattarov, S Sciabola, G De Fabritiis Molecular pharmaceutics 16 (10), 4282-4291, 2019 | 125 | 2019 |
| Coloring molecules with explainable artificial intelligence for preclinical relevance assessment J Jiménez-Luna, M Skalic, N Weskamp, G Schneider Journal of Chemical Information and Modeling 61 (3), 1083-1094, 2021 | 75 | 2021 |
| 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 | 69 | 2019 |
| 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 | 60 | 2019 |
| 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 | 30 | 2022 |
| Building a size constrained predictive model for video classification M Skalic, D Austin Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 0-0, 2018 | 29 | 2018 |
| The inclusive images competition J Atwood, Y Halpern, P Baljekar, E Breck, D Sculley, P Ostyakov, ... The NeurIPS'18 Competition: From Machine Learning to Intelligent …, 2020 | 24 | 2020 |
| Transcriptomics unravels the adaptive molecular mechanisms of Brettanomyces bruxellensis under SO2 stress in wine condition F Valdetara, M Škalič, D Fracassetti, M Louw, C Compagno, M Du Toit, ... Food microbiology 90, 103483, 2020 | 15 | 2020 |
| Deep learning methods for efficient large scale video labeling M Skalic, M Pekalski, XE Pan arXiv preprint arXiv:1706.04572, 2017 | 15 | 2017 |
| Multi‐Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets M Walter, JM Borghardt, L Humbeck, M Skalic Molecular Informatics 43 (10), e202400079, 2024 | 4 | 2024 |
| Fast and accurate differential splicing analysis across multiple conditions with replicates JC Entizne, JL Trincado, G Hysenaj, B Singh, M Skalic, DJ Elliott, E Eyras bioRxiv, 086876, 2016 | 3 | 2016 |
| The Inclusive Images Competition D Sculley, E Breck, I Ivanov, J Atwood, M Skalic, P Baljekar, P Ostyakov, ... | 2 | 2019 |
| Deep learning for drug design: modeling molecular shapes M Skalic Universitat Pompeu Fabra, 2019 | 1 | 2019 |
| In silico PK predictions in Drug Discovery: Benchmarking of Strategies to Integrate Machine Learning with Empiric and Mechanistic PK modelling M Walter, G Aljayyoussi, B Gerner, H Rapp, CS Tautermann, P Balazki, ... bioRxiv, 2024.07. 30.605777, 2024 | | 2024 |
| Benchmarking molecular feature attribution methods with activity cliffs JJ Luna, M Skalic, N Weskamp | | 2021 |
| Learning to Localize Temporal Events in Large-scale Video Data M Bober-Irizar, M Skalic, D Austin arXiv preprint arXiv:1910.11631, 2019 | | 2019 |
| SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions JL Trincado Alonso, JC Entizne, G Hysenaj, B Singh, M Skalic, D Elliott, ... Genome Biology. 2018 Dec; 19 (1): 40, 2018 | | 2018 |
