scholarly journals Driving forces in the origins of life

Open Biology ◽  
2021 ◽  
Vol 11 (2) ◽  
Author(s):  
K. A. Dill ◽  
L. Agozzino
Keyword(s):  
Chemical Reaction ◽  
Driving Forces ◽  
Origins Of Life ◽  
Space Sequence ◽  
Reaction Space ◽  
Physico Chemical ◽  
Functional Pathways ◽  
First World

What were the physico-chemical forces that drove the origins of life? We discuss four major prebiotic ‘discoveries’: persistent sampling of chemical reaction space; sequence-encodable foldable catalysts; assembly of functional pathways; and encapsulation and heritability. We describe how a ‘proteins-first’ world gives plausible mechanisms. We note the importance of hydrophobic and polar compositions of matter in these advances.

scholarly journals Mapping the Space of Chemical Reactions using Attention-Based Neural Networks

2020 ◽  
Author(s):  
Philippe Schwaller ◽  
Daniel Probst ◽  
Alain C. Vaucher ◽  
Vishnu H Nair ◽  
David Kreutter ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reaction Center ◽  
Chemical Reactions ◽  
Chemical Reaction ◽  
Classification Accuracy ◽  
Similarity Searching ◽  
Reaction Space ◽  
Fine Grained ◽  
Visual Clustering ◽  
Better Than

<div><div><div><p>Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. Reaction classes facilitate communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task, requiring the identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction center and the distinction between reactants and reagents. In this work, we show that transformer-based models can infer reaction classes from non-annotated, simple text-based representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints which capture fine-grained differences between reaction classes better than traditional reaction fingerprints. The unprecedented insights into chemical reaction space enabled by our learned fingerprints is illustrated by an interactive reaction atlas providing visual clustering and similarity searching. </p><p><br></p><p>Code: https://github.com/rxn4chemistry/rxnfp</p><p>Tutorials: https://rxn4chemistry.github.io/rxnfp/</p><p>Interactive reaction atlas: https://rxn4chemistry.github.io/rxnfp//tmaps/tmap_ft_10k.html</p></div></div></div>

scholarly journals Thermodynamic Driving Forces and Chemical Reaction Fluxes; Reflections on the Steady State

Molecules ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 699 ◽  
Author(s):  
Miloslav Pekař
Keyword(s):  
Steady State ◽  
Chemical Reaction ◽  
Driving Force ◽  
Driving Forces ◽  
Point Of View ◽  
Fluid Mixtures ◽  
Similar Work ◽  
Chemical Potentials ◽  
Thermodynamic Driving Force ◽  
Reacting Systems

Molar balances of continuous and batch reacting systems with a simple reaction are analyzed from the point of view of finding relationships between the thermodynamic driving force and the chemical reaction rate. Special attention is focused on the steady state, which has been the core subject of previous similar work. It is argued that such relationships should also contain, besides the thermodynamic driving force, a kinetic factor, and are of a specific form for a specific reacting system. More general analysis is provided by means of the non-equilibrium thermodynamics of linear fluid mixtures. Then, the driving force can be expressed either in the Gibbs energy (affinity) form or on the basis of chemical potentials. The relationships can be generally interpreted in terms of force, resistance and flux.

scholarly journals Machine learning in chemical reaction space

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Sina Stocker ◽  
Gábor Csányi ◽  
Karsten Reuter ◽  
Johannes T. Margraf
Keyword(s):  
Machine Learning ◽  
Chemical Reaction ◽  
Organic Molecules ◽  
Methane Combustion ◽  
Open Shell ◽  
Reaction Space ◽  
Multiple Reactions ◽  
Reaction Energies ◽  
Important Basis ◽  
Modern Machine

Abstract Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for ‘reactive’ ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses.

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

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>

scholarly journals Mapping the Space of Chemical Reactions using Attention-Based Neural Networks

2020 ◽  
Author(s):  
Philippe Schwaller ◽  
Daniel Probst ◽  
Alain C. Vaucher ◽  
Vishnu H Nair ◽  
David Kreutter ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reaction Center ◽  
Chemical Reactions ◽  
Chemical Reaction ◽  
Classification Accuracy ◽  
Similarity Searching ◽  
Reaction Space ◽  
Fine Grained ◽  
Visual Clustering ◽  
Better Than

<div><div><div><p>Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. Reaction classes facilitate communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task, requiring the identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction center and the distinction between reactants and reagents. In this work, we show that transformer-based models can infer reaction classes from non-annotated, simple text-based representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints which capture fine-grained differences between reaction classes better than traditional reaction fingerprints. The unprecedented insights into chemical reaction space enabled by our learned fingerprints is illustrated by an interactive reaction atlas providing visual clustering and similarity searching. </p><p><br></p><p>Code: https://github.com/rxn4chemistry/rxnfp</p><p>Tutorials: https://rxn4chemistry.github.io/rxnfp/</p><p>Interactive reaction atlas: https://rxn4chemistry.github.io/rxnfp//tmaps/tmap_ft_10k.html</p></div></div></div>

Beyond the Numbers: Charting Chemical Reaction Space

2013 ◽  
Vol 17 (1) ◽  
pp. 40-46 ◽  
Author(s):  
Paul M. Murray ◽  
Simon N. G. Tyler ◽  
Jonathan D. Moseley
Keyword(s):  
Chemical Reaction ◽  
Reaction Space

scholarly journals Thermodynamic Driving Forces and Chemical Reaction Fluxes; Reflections on the Steady State

2020 ◽  
Author(s):  
Miloslav Pekař
Keyword(s):  
Steady State ◽  
Chemical Reaction ◽  
Driving Force ◽  
Driving Forces ◽  
Point Of View ◽  
Fluid Mixtures ◽  
Similar Work ◽  
Chemical Potentials ◽  
Thermodynamic Driving Force ◽  
Reacting Systems

Molar balances of continuous and batch reacting systems with a simple reaction are analyzed from the point of view of finding relationships between the thermodynamic driving force and the chemical reaction rate. Special attention is focused on steady state, which has been the core subject of previous similar work. It is argued that such relationships should contain, besides the thermodynamic driving force, also a kinetic factor, and are of a specific form for a specific reacting system. More general analysis is provided by means of the non-equilibrium thermodynamics of linear fluid mixtures. Then, the driving force can be expressed either in Gibbs energy (affinity) form or on the basis of chemical potentials. The relationships can be generally interpreted in terms of force-resistance-flux.

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