| On the frustration to predict binding affinities from protein–ligand structures with deep neural networks M Volkov, JA Turk, N Drizard, N Martin, B Hoffmann, Y Gaston-Mathé, ... Journal of medicinal chemistry 65 (11), 7946-7958, 2022 | 133 | 2022 |
| Melloddy: Cross-pharma federated learning at unprecedented scale unlocks benefits in qsar without compromising proprietary information W Heyndrickx, L Mervin, T Morawietz, N Sturm, L Friedrich, A Zalewski, ... Journal of chemical information and modeling 64 (7), 2331-2344, 2023 | 51 | 2023 |
| Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance P Pinel, G Guichaoua, M Najm, S Labouille, N Drizard, Y Gaston‐Mathé, ... Molecular Informatics 42 (4), 2200216, 2023 | 6 | 2023 |
| Molecular assays simulator to unravel predictors hacking in goal-directed molecular generations P Gendreau, JA Turk, N Drizard, VB Ribeiro da Silva, C Descamps, ... Journal of Chemical Information and Modeling 63 (13), 3983-3998, 2023 | 4 | 2023 |
| A Molecular Assays Simulator to Unravel Predictors Hacking in goal-directed molecular generations JA Turk, P Gendreau, N Drizard, Y Gaston-Mathé | 1 | 2022 |
| Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance S Labouille, N Drizard, Y Gaston-Mathé, B Hoffmann, V Stoven | | 2023 |
| Interpreting quantitative structure-activity relationship models to guide drug discovery R Arora, D Israel, JA Turk, C Housseman, S Labouille, V Barros, N Drizard, ... American Chemical Society SciMeetings 3 (1), 2022 | | 2022 |
| Binding Affinities Prediction with Graph Neural Networks from Protein-Ligand Structures M Volkov, JA Turk, N Drizard, N Martin, B Hoffmann, D Rognan | | |
