Exploring the GDB-13 chemical space using deep generative models

Journal of Cheminformatics

Jean-Louis Reymond¹  Thomas Blaschke²  Josep Arús-Pous²  Ola Engkvist²  Hongming Chen²  Silas Ulander³ 

[1] Department of Chemistry and Biochemistry, University of Bern;Hit Discovery, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Gothenburg;Medicinal Chemistry, Cardiovascular, Renal and Metabolism, IMED Biotech Unit, AstraZeneca, Gothenburg;

关键词: Deep learning;    Chemical space exploration;    Deep generative models;    Recurrent neural networks;    Chemical databases;   

DOI  :  10.1186/s13321-019-0341-z

来源: DOAJ

【 摘 要 】

Abstract Recent applications of recurrent neural networks (RNN) enable training models that sample the chemical space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enumerated database GDB-13 (975 million molecules). We show that a model trained with 1 million structures (0.1% of the database) reproduces 68.9% of the entire database after training, when sampling 2 billion molecules. We also developed a method to assess the quality of the training process using negative log-likelihood plots. Furthermore, we use a mathematical model based on the “coupon collector problem” that compares the trained model to an upper bound and thus we are able to quantify how much it has learned. We also suggest that this method can be used as a tool to benchmark the learning capabilities of any molecular generative model architecture. Additionally, an analysis of the generated chemical space was performed, which shows that, mostly due to the syntax of SMILES, complex molecules with many rings and heteroatoms are more difficult to sample.

【 授权许可】

Unknown