Cheminformatic quantum mechanical enzyme model design: a catechol-O-methyltransferase case study

2021 ◽  
Author(s):  
Thomas J. Summers ◽  
Qianyi Cheng ◽  
Manuel A. Palma ◽  
Diem-Trang Pham ◽  
Dudley K. Kelso ◽  
...  
Keyword(s):  
Quantum Mechanical ◽  
Model Design ◽  
Enzyme Model

INNOVATIVE BORN GLOBALS: INVESTIGATING THE INFLUENCE OF THEIR BUSINESS MODELS ON INTERNATIONAL PERFORMANCE

2017 ◽  
Vol 21 (01) ◽  
pp. 1750005 ◽  
Author(s):  
SASCHA KRAUS ◽  
ALEXANDER BREM ◽  
MIRIAM SCHUESSLER ◽  
FELIX SCHUESSLER ◽  
THOMAS NIEMAND
Keyword(s):  
Firm Performance ◽  
Business Model ◽  
Business Models ◽  
Innovation Management ◽  
Model Design ◽  
Born Globals ◽  
International Firm ◽  
Multiple Case ◽  
Business Model Design

Internationalization is a hot topic in innovation management, whereby the phenomenon of “Born Globals” is still limited to research in the domains of Entrepreneurship and International Management. As business model design plays a key role for Born Globals, we link these two concepts. For this, we propose hypotheses about the influence of efficiency-centered and novelty-centered business model design on international firm performance. To test these hypotheses, we performed a quantitative survey with 252 founders of international companies in Germany, Switzerland and Liechtenstein. Additionally, we gained further insights through a case study analysis of 11 Born Globals. The results show that business model design matters to international firm performance and the business model design of Born Globals tends to be more efficiency-centered. Based on a multiple case study, we analyzed business models in a more sophisticated way and derived propositions that yielded in an archetype of a Born Global’s business model.

scholarly journals The Systematic Model (ESFE) for Implementation of Open Innovation in Yuchai Group in the Peopleos Republic of China

2019 ◽  
pp. 1-17
Author(s):  
Lin Zhang ◽  
Xiaojuan Yang ◽  
Dawei Zhang
Keyword(s):  
Open Innovation ◽  
Design Methodology ◽  
Theory Approach ◽  
Model Design ◽  
Republic Of China ◽  
Evaluation Of Performance ◽  
Input And Output ◽  
Innovation Model ◽  
Grounded Theory Approach

Purpose: This paper aims to develop a systematic model of open innovation from a systematic view based on the case study of Yuchai Group‘s practices to illustrate the knowledge input and output in the open-innovation model. Design/methodology/approach: The systematic model of open innovation is constructed based on Yuchai Group‘s practices in the People‘s Republic of China from a grounded theory approach. Findings: The results show that, from the systematic view, the dynamic process of open innovation is divided into four interconnected parts: elements, integration, evaluation of performance and adjustment to the environment. For Yuchai Group, the element acquisition are much more vital than the development of ideas.

scholarly journals Can Local Electric Field be a Descriptor of Catalytic Activity? A Case Study on Chorismate Mutase

2021 ◽  
Author(s):  
Shakir Ali Siddiqui ◽  
Kshatresh Dutta Dubey
Keyword(s):  
Activation Energy ◽  
Catalytic Activity ◽  
Electric Field ◽  
Enzymatic Activity ◽  
Quantum Mechanical Calculation ◽  
Chorismate Mutase ◽  
Quantum Mechanical ◽  
Mechanical Calculation

The current theoretical perception of enzymatic activity is highly reliant on the determination of activation energy of the reactions which is often calculated using a computational demanding quantum mechanical calculation....

Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki–Miyaura cross-coupling reaction

2020 ◽  
Vol 7 (16) ◽  
pp. 2269-2277
Author(s):  
Zunyun Fu ◽  
Xutong Li ◽  
Zhaohui Wang ◽  
Zhaojun Li ◽  
Xiaohong Liu ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Deep Learning ◽  
Coupling Reaction ◽  
Cross Coupling ◽  
Quantum Mechanical ◽  
Reaction Conditions ◽  
Cross Coupling Reaction ◽  
Deep Learning Model ◽  
Optimal Reaction

Deep learning was used to optimize chemical reactions with the quantum mechanical properties of chemical contexts and reaction conditions as inputs. The trained deep learning model determines optimal reaction conditions by in silico exploration of accessible reaction space.

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