If you want to cite the prediction models, please cite the following papers:

Enzyme-Substrate Pair Prediction:

• Kroll, A., Ranjan, S., Engqvist, M. K., & Lercher, M. J. (2023). A general model to predict small molecule substrates of enzymes based on machine and deep learning. Nature Communications, 14(1), 2787.
  DOI: 10.1038/s41467-023-38347-2
• Kroll, A., Ranjan, S., & Lercher, M. J. (2024). A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships. PLOS Computational Biology, 20(5), e1012100.
  DOI: 10.1371/journal.pcbi.1012100

Turnover Number k_cat Prediction:

• Kroll, A., Rousset, Y., Hu, X. P., Liebrand, N. A., & Lercher, M. J. (2023). Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning. Nature Communications, 14(1), 4139.
  DOI: 10.1038/s41467-023-39840-4

Michaelis Constant K_M Prediction:

• Kroll, A., Engqvist, M. K., Heckmann, D., & Lercher, M. J. (2021). Deep learning allows genome-scale prediction of Michaelis constants from structural features. PLoS Biology, 19(10), e3001402.
  DOI: 10.1371/journal.pbio.3001402

Transporter-Substrate Pair Prediction:

• Kroll, A., Niebuhr, N., Butler, G., & Lercher, M. J. (2024). SPOT: A machine learning model that predicts specific substrates for transport proteins. PLoS Biology, 22(9), e3002807.
  DOI: 10.1371/journal.pbio.3002807