Open AccessExploring Chemical Reaction Space with Reaction Difference Fingerprints and Parametric t-SNE
Author(s) -
Mikhail Andronov,
Maxim V. Fedorov,
Sergey Sosnin
Publication year - 2021
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.1c04778
Subject(s) - chemical space , computer science , parametric statistics , space (punctuation) , manifold (fluid mechanics) , chemical reaction , projection (relational algebra) , artificial intelligence , chemistry , algorithm , mathematics , organic chemistry , mechanical engineering , biochemistry , statistics , engineering , drug discovery , operating system
Humans prefer visual representations for the analysis of large databases. In this work, we suggest a method for the visualization of the chemical reaction space. Our technique uses the t-SNE approach that is parameterized using a deep neural network (parametric t-SNE). We demonstrated that the parametric t-SNE combined with reaction difference fingerprints could provide a tool for the projection of chemical reactions on a low-dimensional manifold for easy exploration of reaction space. We showed that the global reaction landscape projected on a 2D plane corresponds well with the already known reaction types. The application of a pretrained parametric t-SNE model to new reactions allows chemists to study these reactions in a global reaction space. We validated the feasibility of this approach for two commercial drugs, darunavir and montelukast. We believe that our method can help to explore reaction space and will inspire chemists to find new reactions and synthetic ways.