1. Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T. A guide to machine learning for biologists. Nat. Rev. Mol. Cell Biol. 23, 40–55 (2022).
2. Yu, M. K. et al. Visible machine learning for biomedicine. Cell 173, 1562–1565 (2018).
3. Wu, Z. et al. MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2017).
4. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. 34th International Conference on Machine Learning: Proc. Machine Learning Research Vol. 70 (eds Precup, D. & Teh, Y. W.) 1263–1272 (PMLR, 2017).
5. Sanchez-Gonzalez, A. et al. Graph networks as learnable physics engines for inference and control. In Proc. 35th International Conference on Machine Learning: Proc. Machine Learning Research Vol. 80 (eds Dy, J. & Krause, A.) 4470–4479 (PMLR, 2018).