Deep generative models such as generative adversarial networks, variational autoencoders, and autoregressive models are rapidly growing in popularity for the discovery of new molecules and materials. In this work, we introduce MOlecular SEtS (MOSES), a benchmarking platform to support research on machine learning for drug discovery. MOSES implements several popular molecular generation models and includes a set of metrics that evaluate the diversity and quality of generated molecules. MOSES is meant to standardize the research on the molecular generation and facilitate the sharing and comparison of new models. Additionally, we provide a large-scale comparison of existing state of the art models and elaborate on current challenges for generative models that might prove fertile ground for new research. Our platform and source code are freely available at https://github.com/molecularsets/

通过建立叫做MOSES的基准平台来标准化分子生成模型的训练和比较，该平台提供了训练和测试数据集，并提供一组指标来评估生成结构的质量和多样性。我们实施并比较了几种生成模型，并建议将我们的结果用作进一步发展生成化学研究的参考点。

分子集(MOSES)：分子生成模型基准测试平台