scholarly journals Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

2019 ◽  
Vol 10 (27) ◽  
pp. 6697-6706 ◽  
Author(s):  
Yehia Amar ◽  
Artur M. Schweidtmann ◽  
Paul Deutsch ◽  
Liwei Cao ◽  
Alexei Lapkin
Keyword(s):  
Machine Learning ◽  
Asymmetric Catalysis ◽  
Molecular Descriptors ◽  
Process Development ◽  
Solvent Selection ◽  
Significant Challenge

Rational solvent selection remains a significant challenge in process development.

scholarly journals From sugars to FDCA: a techno-economic assessment using a design concept based on solvent selection and carbon dioxide emissions

2021 ◽  
Author(s):  
Amir Al Ghatta ◽  
James D. E. T. Wilton-Ely ◽  
Jason P. Hallett
Keyword(s):  
Carbon Dioxide ◽  
Carbon Dioxide Emissions ◽  
Process Development ◽  
Economic Assessment ◽  
Design Concept ◽  
Selling Price ◽  
Solvent Selection ◽  
Current Process ◽  
Process Simulations ◽  
Solvent Systems

Process simulations allow the evaluation of the emissions and selling price for the production of the key monomer FDCA based on different feedstocks and solvent systems, alongside considerations of safety and current process development.

scholarly journals Reaction-Based Machine Learning Representations for Predicting the Enantioselectivity of Organocatalysts

2021 ◽  
Author(s):  
Simone Gallarati ◽  
Raimon Fabregat ◽  
Ruben Laplaza ◽  
Sinjini Bhattacharjee ◽  
Matthew D. Wodrich ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Purity ◽  
Computational Design ◽  
Chiral Compounds ◽  
Significant Challenge

HHundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically...

scholarly journals A Molecular Stereostructure Descriptor Based On Spherical Projection

Synlett ◽  
2020 ◽  
Author(s):  
Shuo-Qing Zhang ◽  
Xin Hong ◽  
Li-Cheng Xu ◽  
Xin Li ◽  
Miao-Jiong Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phosphoric Acid ◽  
Asymmetric Catalysis ◽  
Van Der Waals ◽  
Chiral Phosphoric Acid ◽  
Key Features ◽  
Precise Representation ◽  
Spherical Projection

AbstractDescription of molecular stereostructure is critical for the machine learning prediction of asymmetric catalysis. Herein we report a spherical projection descriptor of molecular stereostructure (SPMS), which allows precise representation of the molecular van der Waals (vdW) surface. The key features of SPMS descriptor are presented using the examples of chiral phosphoric acid, and the machine learning application is demonstrated in Denmark’s dataset of asymmetric thiol addition to N-acylimines. In addition, SPMS descriptor also offers a color-coded diagram that provides straightforward chemical interpretation of the steric environment.

scholarly journals A Sound-based Machine Learning to Predict Traffic Vehicle Density

2021 ◽  
Vol 9 (1) ◽  
pp. 55-62
Author(s):  
Geoferleen Flores ◽  
◽  
Eduardo Jr. Piedad ◽  
Anzeneth Figueroa ◽  
Romari Tumamak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Data Gathering ◽  
Sound Intensity ◽  
Support Vector ◽  
Mean Square ◽  
Significant Challenge ◽  
Vehicle Density ◽  
Smart Technologies ◽  
Machine Learning Models

Traffic flow mismanagement is a significant challenge in all countries especially in crowded cities. An alternative solution is to utilize smart technologies to predict traffic flow. In this study, frequency spectrum describing traffic sound characteristics is used as an indicator to predict the next five-minute vehicle density. Sound frequency and vehicle intensity are collected during a thirteen-hour data gathering. The collected sound intensity and frequency are then used to learn three machine-learning models - support vector machine, artificial neural network, and random forest and to predict vehicle intensity. It was found out that the performances of the three models based on root-mean-square-error values are 12.97, 16.01, and 10.67, respectively. These initial and satisfactory results pave a new way to predict traffic flow based on traffic sound characteristics which may serve as a better alternative to conventional features.

scholarly journals Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes

2020 ◽  
Author(s):  
Xinzhe Zhu ◽  
Chi-Hung Ho ◽  
Xiaonan Wang
Keyword(s):  
Machine Learning ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Production Process ◽  
Environmental Impacts ◽  
High Throughput ◽  
High Throughput Screening ◽  
Molecular Descriptors ◽  
Trifluoroacetic Anhydride ◽  
Chemical Materials

<p><a></a><a>The production process of many active pharmaceutical ingredients such as sitagliptin could cause severe environmental problems due to the use of toxic chemical materials and production infrastructure, energy consumption and wastes treatment. The environmental impacts of sitagliptin production process were estimated with life cycle assessment (LCA) method, which suggested that the use of chemical materials provided the major environmental impacts. Both methods of Eco-indicator 99 and ReCiPe endpoints confirmed that chemical feedstock accounted 83% and 70% of life-cycle impact, respectively. Among all the chemical materials used in the sitagliptin production process, </a><a>trifluoroacetic anhydride </a>was identified as the largest influential factor in most impact categories according to the results of ReCiPe midpoints method. Therefore, high-throughput screening was performed to seek for green chemical substitutes to replace the target chemical (i.e. trifluoroacetic anhydride) by the following three steps. Firstly, thirty most similar chemicals were obtained from two million candidate alternatives in PubChem database based on their molecular descriptors. Thereafter, deep learning neural network models were developed to predict life-cycle impact according to the chemicals in Ecoinvent v3.5 database with known LCA values and corresponding molecular descriptors. Finally, 1,2-ethanediyl ester was proved to be one of the potential greener substitutes after the LCA data of these similar chemicals were predicted using the well-trained machine learning models. The case study demonstrated the applicability of the novel framework to screen green chemical substitutes and optimize the pharmaceutical manufacturing process.</p>

scholarly journals Machine Learning Enhanced Spectroscopic Analysis: Towards Autonomous Chemical Mixture Characterization for Rapid Process Optimization

2022 ◽  
Author(s):  
Andrea Angulo ◽  
Lankun Yang ◽  
Eray S Aydil ◽  
Miguel A. Modestino
Keyword(s):  
Machine Learning ◽  
Process Optimization ◽  
Parameter Space ◽  
Chemical Process ◽  
Spectroscopic Analysis ◽  
Process Development ◽  
Operating Parameter ◽  
Optimization Methods ◽  
Chemical Mixture ◽  
Mixture Characterization

Autonomous chemical process development and optimization methods use algorithms to explore the operating parameter space based on feedback from experimentally determined exit stream compositions. Measuring the compositions of multicomponent streams...

scholarly journals A Novel Stacked Ensemble for Hate Speech Recognition

2021 ◽  
Vol 11 (24) ◽  
pp. 11684
Author(s):  
Mona Khalifa A. Aljero ◽  
Nazife Dimililer
Keyword(s):  
Machine Learning ◽  
Hate Speech ◽  
State Of The Art ◽  
Majority Voting ◽  
Support Vector ◽  
Timely Manner ◽  
Machine Learning Classifiers ◽  
Significant Challenge ◽  
Final Output ◽  
Harmful Content

Detecting harmful content or hate speech on social media is a significant challenge due to the high throughput and large volume of content production on these platforms. Identifying hate speech in a timely manner is crucial in preventing its dissemination. We propose a novel stacked ensemble approach for detecting hate speech in English tweets. The proposed architecture employs an ensemble of three classifiers, namely support vector machine (SVM), logistic regression (LR), and XGBoost classifier (XGB), trained using word2vec and universal encoding features. The meta classifier, LR, combines the outputs of the three base classifiers and the features employed by the base classifiers to produce the final output. It is shown that the proposed architecture improves the performance of the widely used single classifiers as well as the standard stacking and classifier ensemble using majority voting. We also present results on the use of various combinations of machine learning classifiers as base classifiers. The experimental results from the proposed architecture indicated an improvement in the performance on all four datasets compared with the standard stacking, base classifiers, and majority voting. Furthermore, on three of these datasets, the proposed architecture outperformed all state-of-the-art systems.

scholarly journals Machine learning for asymmetric catalysis

Science ◽  
2020 ◽  
Vol 367 (6478) ◽  
pp. 638.5-639
Author(s):  
Jake Yeston
Keyword(s):  
Machine Learning ◽  
Asymmetric Catalysis

Q-SA√ESS: a methodology to help solvent selection for pharmaceutical manufacture at the early process development stage

2016 ◽  
Vol 18 (24) ◽  
pp. 6564-6572 ◽  
Author(s):  
Valerio Isoni ◽  
Loretta L. Wong ◽  
Hsien H. Khoo ◽  
Iskandar Halim ◽  
Paul Sharratt
Keyword(s):  
Manufacturing Process ◽  
Process Development ◽  
Development Stage ◽  
Solvent Selection ◽  
Pharmaceutical Manufacture ◽  
Selection For

A practicable, LCA based methodology has been developed to evaluate the sustainability implications of solvent selection during early process development for a batch manufacturing process for an API.

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