| 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 | 686 | 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 | 382 | 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 | 189 | 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 | 87 | 2019 |
| 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 | 58 | 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 | 55 | 2021 |
| 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 | 54 | 2019 |
| 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 | 25 | 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 | 22 | 2020 |
| 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 | 21 | 2022 |
| Deep learning methods for efficient large scale video labeling M Skalic, M Pekalski, XE Pan arXiv preprint arXiv:1706.04572, 2017 | 14 | 2017 |
| 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 | 12 | 2020 |
| 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 |
| Deep learning for drug design: modeling molecular shapes M Skalic Universitat Pompeu Fabra, 2019 | 1 | 2019 |
| The Inclusive Images Competition D Sculley, E Breck, I Ivanov, J Atwood, M Skalic, P Baljekar, P Ostyakov, ... | 1 | 2019 |
| 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 |
| Deep Learning Methods for Efficient Large Scale Video Labeling M Pękalski, X Pan, M Skalic | | 2017 |
| Integracija bioloških podatkov v napovedni model za odkrivanje molekulskih interakcij pri parodontozi: magistrsko delo M Škalič Univerza v Ljubljani, Biotehniška fakulteta, 2016 | | 2016 |
