Research in natural science disciplines has routinely produced efforts to benchmark computational methods. The area of computational chemistry has relied for decades on thorough bechmarking studies to catalog the accuracy and expense of molecular mechanical, semi-empirical and ab initio quantum mechanical methods. Machine learning researchers customarily use datasets such as MNIST and CIFAR-10 46 to contextualize the performance of novel algorithms. These benchmarks have provided a common ground to compare different approaches and quantitatively measure progress. The chemical machine learning community has thus built similar benchmark datasets, such as MoleculeNet, the QMx series, and the Therapeutics Data Commons, among others. Specialized benchmarks for generative models and inverse molecular design have also been developed, including GuacaMol, Moses, PMO, and Tartarus .