Synthetic Route Design of AZD4635, an A2AR Antagonist

2019 ◽  
Vol 23 (7) ◽  
pp. 1407-1419 ◽  
Author(s):  
Mairi M. Littleson ◽  
Andrew D. Campbell ◽  
Adam Clarke ◽  
Mark Dow ◽  
Gareth Ensor ◽  
...  
Keyword(s):  
Synthetic Route ◽  
Route Design

Making Better Decisions During Synthetic Route Design: Leveraging Prediction to Achieve Greenness-by-Design

2019 ◽  
Author(s):  
Jun Li ◽  
Martin Eastgate
Keyword(s):  
Proof Of Concept ◽  
Synthetic Route ◽  
High Complexity ◽  
Route Design ◽  
Synthetic Routes ◽  
Catalyzed Reactions ◽  
Metal Catalyzed ◽  
Potential Efficiency

This paper expands our work predicting Process Mass Intensity (PMI), as a methodology for exploring the potential efficiency of proposed synthetic routes. In the present work, we integrate a method for predicting the PMI contributions of high complexity reagents, needed to enable certain transformations. We focus on ligands for metal catalyzed reactions - and develop an approach for predicting which ligands may function in CN couplings - as a proof of concept. We leverage this to enable the integration of the PMI contribution of the ligands into a predictions of a routes efficiency, enabling an understanding of the holistic impact of a route decision..

Development of Evaluation Model for Strategic Sites in Synthetic Route Design System AIPHOS

2008 ◽  
Vol 9 ◽  
pp. 81-91
Author(s):  
Akio Tanaka ◽  
Takashi Kawai ◽  
Tsutomu Matsumoto ◽  
Tetsuhiko Takabatake ◽  
Hideho Okamoto ◽  
...  
Keyword(s):  
Evaluation Model ◽  
Design System ◽  
Synthetic Route ◽  
Route Design

scholarly journals AI-Driven Synthetic Route Design with Retrosynthesis Knowledge

2020 ◽  
Author(s):  
Shoichi Ishida ◽  
Kei Terayama ◽  
Ryosuke Kojima ◽  
Kiyosei Takasu ◽  
Yasushi Okuno
Keyword(s):  
Deep Neural Networks ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Synthetic Route ◽  
Learning Techniques ◽  
Route Design ◽  
Human Interventions ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis ◽  
Synthetic Routes

<div>Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they exploit their experiments, intuition, and knowledge. Recent breakthroughs in machine learning techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human interventions. However, such CASP applications are yet to incorporate retrosynthesis knowledge sufficiently into their algorithms to reflect chemists' way of thinking flexibly. In this study, we developed a hybrid CASP application of data-driven techniques and various retrosynthesis knowledge called "ReTReK" that integrates the knowledge as adjustable parameters into an evaluation for promising search directions. Experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, and the results indicated that the synthetic routes searched with the knowledge were preferred to those without knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into data-driven CASP applications is expected to contribute to further their development and spread them to chemists widely. </div>

Making Better Decisions During Synthetic Route Design: Leveraging Prediction to Achieve Greenness-by-Design

2019 ◽  
Author(s):  
Jun Li ◽  
Martin Eastgate
Keyword(s):  
Proof Of Concept ◽  
Synthetic Route ◽  
High Complexity ◽  
Route Design ◽  
Synthetic Routes ◽  
Catalyzed Reactions ◽  
Metal Catalyzed ◽  
Potential Efficiency

This paper expands our work predicting Process Mass Intensity (PMI), as a methodology for exploring the potential efficiency of proposed synthetic routes. In the present work, we integrate a method for predicting the PMI contributions of high complexity reagents, needed to enable certain transformations. We focus on ligands for metal catalyzed reactions - and develop an approach for predicting which ligands may function in CN couplings - as a proof of concept. We leverage this to enable the integration of the PMI contribution of the ligands into a predictions of a routes efficiency, enabling an understanding of the holistic impact of a route decision..

scholarly journals AI-Driven Synthetic Route Design with Retrosynthesis Knowledge

2020 ◽  
Author(s):  
Shoichi Ishida ◽  
Kei Terayama ◽  
Ryosuke Kojima ◽  
Kiyosei Takasu ◽  
Yasushi Okuno
Keyword(s):  
Deep Neural Networks ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Synthetic Route ◽  
Learning Techniques ◽  
Route Design ◽  
Human Interventions ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis ◽  
Synthetic Routes

<div>Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they exploit their experiments, intuition, and knowledge. Recent breakthroughs in machine learning techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human interventions. However, such CASP applications are yet to incorporate retrosynthesis knowledge sufficiently into their algorithms to reflect chemists' way of thinking flexibly. In this study, we developed a hybrid CASP application of data-driven techniques and various retrosynthesis knowledge called "ReTReK" that integrates the knowledge as adjustable parameters into an evaluation for promising search directions. Experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, and the results indicated that the synthetic routes searched with the knowledge were preferred to those without knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into data-driven CASP applications is expected to contribute to further their development and spread them to chemists widely. </div>

Making better decisions during synthetic route design: leveraging prediction to achieve greenness-by-design

2019 ◽  
Vol 4 (9) ◽  
pp. 1595-1607 ◽  
Author(s):  
Jun Li ◽  
Martin D. Eastgate
Keyword(s):  
Conceptual Framework ◽  
Synthetic Route ◽  
Route Design

A conceptual framework for incorporating machine learned ligand prediction into predictive route comparisons, to enable greener chemistry outcomes.

A General Convergent Strategy for the Synthesis of Tetrasubstituted Furan Fatty Acids (FuFAs)

2019 ◽  
Author(s):  
Yamin Wang ◽  
Gareth Pritchard ◽  
Marc Kimber
Keyword(s):  
Fatty Acids ◽  
Spectral Data ◽  
Synthetic Route ◽  
Furan Fatty Acids

Synthetic route for the synthesis of tetrasubstituted furan fatty acids; including experimental details, characterisation, and spectral data of all intermediates.

Palladium-Catalyzed Asymmetric Annulation Between Aryl Iodides and Racemic Epoxides Using a Chiral Norbornene Cocatalyst

2018 ◽  
Author(s):  
Guangbin Dong ◽  
Renhe Li
Keyword(s):  
Isopropyl Ester ◽  
Synthetic Route ◽  
Initial Development ◽  
Aryl Iodides ◽  
Palladium Catalyzed

Herein, we describe our initial development of an asymmetric Pd-catalyzed annulation between aryl iodides and racemic epoxides for synthesis of 2,3-dihydrobenzofurans using a chiral norbornene cocatalyst. A series of enantiopure ester-, amide- and imide-substituted norbornenes have been prepared with a reliable synthetic route. Promising enantioselectivity (42-45% ee) has been observed using the isopropyl ester-substituted norbornene (N1*) and the amide-substituted norbornene (N7*).

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