| Deep learning allows genome-scale prediction of Michaelis constants from structural features A Kroll, MKM Engqvist, D Heckmann, MJ Lercher PLoS biology 19 (10), e3001402, 2021 | 57 | 2021 |
| A general model to predict small molecule substrates of enzymes based on machine and deep learning A Kroll, S Ranjan, MKM Engqvist, MJ Lercher Nature communications 14 (1), 2787, 2023 | 36* | 2023 |
| Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning A Kroll, Y Rousset, XP Hu, NA Liebrand, MJ Lercher Nature Communications 14 (1), 4139, 2023 | 23 | 2023 |
| Drug-target interaction prediction using a multi-modal transformer network demonstrates high generalizability to unseen proteins A Kroll, S Ranjan, MJ Lercher bioRxiv, 2023.08. 21.554147, 2023 | 2* | 2023 |
| Machine learning models for the prediction of enzyme properties should be tested on proteins not used for model training A Kroll, MJ Lercher bioRxiv, 2023.02. 06.526991, 2023 | 2 | 2023 |
| A general prediction model for substrates of transport proteins A Kroll, N Niebuhr, G Butler, MJ Lercher bioRxiv, 2023.10. 31.564943, 2023 | | 2023 |
| Prediction of enzyme kinetic parameters and substrate scopes using artificial intelligence A Kroll | | |
