Engqvist, Martin K. M.

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Li, F., Yuan, L., Lu, H., Li, G., Chen, Y., Engqvist, M. K. M., Kerkhoven, E. J., & Nielsen, J. (2022) · Nature Catalysis

Microbial Metabolic Engineering and BioproductionBioinformatics and Genomic Networks

Cited by 466

Expanding functional protein sequence spaces using generative adversarial networks

Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W., et al. (2021) · Nature Machine Intelligence

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 388

A general model to predict small molecule substrates of enzymes based on machine and deep learning

Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J. (2023) · Nature Communications

Machine Learning in BioinformaticsComputational Drug Discovery Methods

Cited by 193

Deep learning allows genome-scale prediction of Michaelis constants from structural features

Kroll, A., Engqvist, M. K. M., Heckmann, D., & Lercher, M. J. (2021) · PLOS Biology

Microbial Metabolic Engineering and BioproductionMachine Learning in Bioinformatics

Cited by 132

Ribonucleotides incorporated by the yeast mitochondrial DNA polymerase are not repaired

Wanrooij, P. H., Engqvist, M. K. M., Forslund, J. M. E., Navarrete, C., Nilsson, A. K., Sedman, J., Wanrooij, S., Clausen, A. R., & Chabes, A. (2017) · Proceedings of the National Academy of Sciences

DNA Repair MechanismsMitochondrial Function and Pathology

Cited by 48