The Estimation of Intrinsic Low Dimensional Manifold Dimension in Atmospheric Chemical Reaction Systems

<div>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 by a deep neural network (parametric t-SNE). We demonstrated that the parametric t-SNE combined with reaction difference fingerprints can provide a tool for the projection of chemical reactions onto a low-dimensional manifold for easy exploration of reaction space. We showed that the global reaction landscape, been projected onto a 2D plane, corresponds well with already known reaction types. The application of a pretrained parametric t-SNE model to new reactions allows chemists to study these reactions on a global reaction space. We validated the feasibility of this approach for two marketed drugs: darunavir and oseltamivir. We believe that our method can help explore reaction space and inspire chemists to find new reactions and synthetic ways. </div><div><br></div>

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Exploring Chemical Reaction Space With Reaction Difference Fingerprints and Parametric t-SNE

10.26434/chemrxiv.13929320.v2 ◽

2021 ◽

Author(s):

Mikhail Andronov ◽

Maxim Fedorov ◽

Sergey Sosnin

Keyword(s):

Neural Network ◽

Chemical Reaction ◽

Deep Neural Network ◽

Visual Representations ◽

Dimensional Manifold ◽

Reaction Space ◽

Large Databases ◽

Global Reaction ◽

Low Dimensional ◽

Low Dimensional Manifold

<div>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 by 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 onto a low-dimensional manifold for easy exploration of reaction space. We showed that the global reaction landscape, been projected onto a 2D plane, corresponds well with 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 marketed drugs: darunavir and oseltamivir. We believe that our method can help to explore reaction space and will inspire chemists to find new reactions and synthetic ways. </div><div><br></div>

Download Full-text

Exploring Chemical Reaction Space With Reaction Difference Fingerprints and Parametric t-SNE

10.26434/chemrxiv.13929320 ◽

2021 ◽

Author(s):

Mikhail Andronov ◽

Maxim Fedorov ◽

Sergey Sosnin

Keyword(s):

Neural Network ◽

Chemical Reaction ◽

Deep Neural Network ◽

Visual Representations ◽

Dimensional Manifold ◽

Reaction Space ◽

Large Databases ◽

Global Reaction ◽

Low Dimensional ◽

Low Dimensional Manifold

<div>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 by a deep neural network (parametric t-SNE). We demonstrated that the parametric t-SNE combined with reaction difference fingerprints can provide a tool for the projection of chemical reactions onto a low-dimensional manifold for easy exploration of reaction space. We showed that the global reaction landscape, been projected onto a 2D plane, corresponds well with already known reaction types. The application of a pretrained parametric t-SNE model to new reactions allows chemists to study these reactions on a global reaction space. We validated the feasibility of this approach for two marketed drugs: darunavir and oseltamivir. We believe that our method can help explore reaction space and inspire chemists to find new reactions and synthetic ways. </div><div><br></div>

Download Full-text