Journal Article

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Zhebrak, Alexander, Artamonov, Aleksey, Tatanov, Oktai, Kurbanov, Rauf, Aladinskiy, Vladimir, Johansson, Simon, Zhavoronkov, Alex, Belyaev, Stanislav, Sanchez-Lengeling, Benjamin, Polykovskiy, Daniil, Golovanov, Sergey, Kadurin, Artur, Chen, Hongming, Aspuru-Guzik, Alán, Veselov, Mark, Nikolenko, Sergey

2020

Overview

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-supervized 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 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 .

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: benchmark / Datasets / Deep learning / Digital libraries / distribution learning / Drug development / drug discovery / generative models / Graph representations / Machine learning / Molecular structure / Pharmacology / Prediction models / Syntax / Toxicity / Validity