| 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 | 100 | 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 | 84 | 2023 |
| 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 | 83 | 2021 |
| A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships A Kroll, S Ranjan, MJ Lercher PLOS Computational Biology 20 (5), e1012100, 2024 | 11* | 2024 |
| DLKcat cannot predict meaningful kcat values for mutants and unfamiliar enzymes A Kroll, MJ Lercher Biology Methods and Protocols 9 (1), bpae061, 2024 | 10* | 2024 |
| SPOT: A machine learning model that predicts specific substrates for transport proteins A Kroll, N Niebuhr, G Butler, MJ Lercher PLoS biology 22 (9), e3002807, 2024 | 2* | 2024 |
| Transfer learning improves predictions of enzyme kinetic parameters A Kroll Chem Catalysis 4 (9), 2024 | | 2024 |
| Prediction of enzyme kinetic parameters and substrate scopes using artificial intelligence A Kroll Dissertation, Düsseldorf, Heinrich-Heine-Universität, 2023, 2024 | | 2024 |
Martin J. LercherProfessor of Computational Cell Biology, Heinrich Heine University DüsseldorfEmail yang diverifikasi di hhu.de
