Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
This work addresses the problem of inconsistent evaluation for researchers in generative chemistry, though it is incremental as it builds on existing models and datasets.
The authors tackled the lack of standardized comparison for molecular generative models by introducing the Molecular Sets (MOSES) benchmarking platform, which includes datasets and metrics, and they implemented and compared several models to provide reference points.
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.