| Efficient generative modeling of protein sequences using simple autoregressive models J Trinquier, G Uguzzoni, A Pagnani, F Zamponi, M Weigt Nature communications 12 (1), 5800, 2021 | 84 | 2021 |
| Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems S Ciarella, J Trinquier, M Weigt, F Zamponi Machine Learning: Science and Technology 4 (1), 010501, 2023 | 28 | 2023 |
| SWAMPNN: End-to-end protein structures alignment J Trinquier, S Petti, S Feng, J Söding, M Steinegger, S Ovchinnikov Machine Learning for Structural Biology Workshop, NeurIPS, 2022 | 3 | 2022 |
| Where the really hard sampling problems are S Ciarella, J Trinquier, M Weigt, F Zamponi arXiv e-prints, arXiv: 2210.11145, 2022 | 1 | 2022 |
| Data-driven generative modeling of protein sequence landscapes and beyond J Trinquier Sorbonne Université, 2023 | | 2023 |
| Author Correction: Efficient generative modeling of protein sequences using simple autoregressive models J Trinquier, G Uguzzoni, A Pagnani, F Zamponi, M Weigt Nature Communications 13 (1), 1889, 2022 | | 2022 |
