Deep generative models are rapidly becoming popular for the discovery of new molecules and materials. Such models learn on a large collection of molecular structures and produce novel compounds. 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 provides a set of metrics to evaluate the quality and diversity of generated molecules. With MOSES, we aim to standardize the research on molecular generation and facilitate the sharing and comparison of new models.
module spider moses to find out what environment modules are available for this application.
- HPC_MOSES_DIR - installation directory
If you publish research that uses moses you have to cite it as follows:
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models, Polykovskiy, Daniil and Zhebrak, Alexander and Sanchez-Lengeling, Benjamin and Golovanov, Sergey and Tatanov, Oktai and Belyaev, Stanislav and Kurbanov, Rauf and Artamonov, Aleksey and Aladinskiy, Vladimir and Veselov, Mark and Kadurin, Artur and Johansson, Simon and Chen, Hongming and Nikolenko, Sergey and Aspuru-Guzik, Alan and Zhavoronkov, Alex, Frontiers in Pharmacology, 2020