  Generative models are becoming a tool of choice for exploring the molecular
space. These models learn on a large training dataset and produce novel
molecular structures with similar properties. Generated structures can be
utilized for virtual screening or training semi-supervised predictive models in
the downstream tasks. While there are plenty of generative models, it is
unclear how to compare and rank them. In this work, we introduce a benchmarking
platform called Molecular Sets (MOSES) to standardize training and comparison
of molecular generative models. MOSES provides a training and testing datasets,
and a set of metrics to evaluate the quality and diversity of generated
structures. We have implemented and compared several molecular generation
models and suggest to use our results as reference points for further
advancements in generative chemistry research. The platform and source code are
available at https://github.com/molecularsets/moses.
