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

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..

Implementation of Airborne Remote Sensing Route Design System

2012 ◽  
Vol 588-589 ◽  
pp. 1171-1177
Author(s):  
Guo Dong Yang ◽  
Guang Nan Liu ◽  
Yuan Yuan Zhang
Keyword(s):  
Remote Sensing ◽  
Geographic Information ◽  
Design Methods ◽  
Design System ◽  
Airborne Remote Sensing ◽  
Route Design ◽  
Main Target

Route design is an important part of airborne remote sensing. On the basis of DEM, using raster and vector geographic information, designing aerial route quickly and accurately is the main target of this system. Considering the actual requests, this system formulates two modes of aerial route design methods: according to navigating area and two points to design. This paper mainly introduces functions of system and algorithms of aerial route design methods.

scholarly journals Experimental and Numerical Investigation of Heat Transfer on a Cooled Turbine Vane

1998 ◽  
Author(s):  
Frank G. Rubensdörffer ◽  
Christer S. Hjalmarsson
Keyword(s):  
Heat Transfer ◽  
Evaluation Model ◽  
Navier Stokes ◽  
Design System ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Component Test ◽  
Turbine Vane ◽  
Dimensional Boundary ◽  
Cooling Design

An experimental research program was performed in a pressurized hot component test rig to evaluate surface temperatures, cooling effectiveness and heat transfer coefficients on a convective cooled turbine vane at near engine conditions. The results were obtained in a 40° five-vane sector cascade at exit Reynolds numbers of 0.98*106 and 2.28*106 at a turbulence level of 4 %. The tests were performed both in steady state and in transient modes. The temperature solving part of a cooling design system was used to evaluate the heat transfer coefficients. A two-dimensional boundary layer program and the commercial Navier-Stokes code Rampant were used for obtaining the numerical results. This paper describes the experimental procedure, the evaluation model and method and the comparison of the first experimental results with numerical calculations.

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.

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