scholarly journals Predicting Glycosylation Stereoselectivity Using Machine Learning

2021 ◽  
Author(s):  
Soo-Yeon Moon ◽  
Sourav Chatterjee ◽  
Peter Seeberger ◽  
Kerry Gilmore
Keyword(s):  
Machine Learning ◽  
Chemical Reactions

Predicting the stereochemical outcome of chemical reactions is challenging in mechanistically ambiguous transformations. The stereoselectivity of glycosylation reactions is influenced by at least eleven factors across four chemical participants and...

scholarly journals Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space

2021 ◽  
Vol 155 (6) ◽  
pp. 064105
Author(s):  
Stefan Heinen ◽  
Guido Falk von Rudorff ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Machine Learning ◽  
Chemical Reactions ◽  
Learning Barriers

scholarly journals When machine learning meets multiscale modeling in chemical reactions

2020 ◽  
Vol 153 (9) ◽  
pp. 094117
Author(s):  
Wuyue Yang ◽  
Liangrong Peng ◽  
Yi Zhu ◽  
Liu Hong
Keyword(s):  
Machine Learning ◽  
Multiscale Modeling ◽  
Chemical Reactions

scholarly journals Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

2020 ◽  
Author(s):  
Gabriel dos Passos Gomes ◽  
Robert Pollice ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Chemical Reactions ◽  
Catalyst Design ◽  
New Drugs ◽  
Homogeneous Catalyst ◽  
Chemical Bonds ◽  
Catalytic Systems ◽  
Data Intensive ◽  
The Past

<div><div><div><p>The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our life-expectancies through the synthesis of new drugs. However, developing new chemical reactions and catalytic systems is a tedious task that requires tremendous discovery and optimization efforts. Over the past decade, advances in machine learning have revolutionized a whole new way to approach data- intensive problems, and many of these developments have started to enter chemistry. However, similar progress in the field of homogenous catalysis are only in their infancy. In this article, we want to outline our vision for the future of catalyst design and the role of machine learning to navigate this maze.</p></div></div></div>

scholarly journals Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

2020 ◽  
Author(s):  
Gabriel dos Passos Gomes ◽  
Robert Pollice ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Chemical Reactions ◽  
Catalyst Design ◽  
New Drugs ◽  
Homogeneous Catalyst ◽  
Chemical Bonds ◽  
Catalytic Systems ◽  
Data Intensive ◽  
The Past

<div><div><div><p>The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our life-expectancies through the synthesis of new drugs. However, developing new chemical reactions and catalytic systems is a tedious task that requires tremendous discovery and optimization efforts. Over the past decade, advances in machine learning have revolutionized a whole new way to approach data- intensive problems, and many of these developments have started to enter chemistry. However, similar progress in the field of homogenous catalysis are only in their infancy. In this article, we want to outline our vision for the future of catalyst design and the role of machine learning to navigate this maze.</p></div></div></div>

Machine learning of generic reactions: 2. toward an advanced computer representation of chemical reactions

1990 ◽  
Vol 3 (6) ◽  
pp. 335-349 ◽  
Author(s):  
Philippe Jauffret ◽  
Christian Tonnelier ◽  
Thierry Hanser ◽  
Gérard Kaufmann ◽  
Robert Wolff
Keyword(s):  
Machine Learning ◽  
Chemical Reactions ◽  
Computer Representation ◽  
Advanced Computer
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