Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors

2021 ◽  
Author(s):  
Zhongyu Wan ◽  
Quan-De Wang ◽  
Dongchang Liu ◽  
Jinhu Liang
Keyword(s):  
Machine Learning ◽  
Optimal Synthesis ◽  
Human Knowledge ◽  
Crucial Importance ◽  
Reactivity Descriptors ◽  
Synthesis Conditions ◽  
Wide Range ◽  
Synthesis Reactions ◽  
Selection Of

Enzyme-catalyzed synthesis reactions are of crucial importance for a wide range of applications. An accurate and rapid selection of optimal synthesis conditions is crucial and challenging for both human knowledge...

COMBINATORIAL POLYNOMIALLY COMPUTABLE CHARACTERISTICS OF SUBSTITUTIONS AND THEIR PROPERTIES

2020 ◽  
Vol 7 (2) ◽  
pp. 34-41
Author(s):  
VLADIMIR NIKONOV ◽  
◽  
ANTON ZOBOV ◽  
Keyword(s):  
Mathematical Expectation ◽  
Point Of View ◽  
Small Range ◽  
Computational Point ◽  
Wide Range ◽  
Bijective Function ◽  
The Difference ◽  
Element Base ◽  
Selection Of

The construction and selection of a suitable bijective function, that is, substitution, is now becoming an important applied task, particularly for building block encryption systems. Many articles have suggested using different approaches to determining the quality of substitution, but most of them are highly computationally complex. The solution of this problem will significantly expand the range of methods for constructing and analyzing scheme in information protection systems. The purpose of research is to find easily measurable characteristics of substitutions, allowing to evaluate their quality, and also measures of the proximity of a particular substitutions to a random one, or its distance from it. For this purpose, several characteristics were proposed in this work: difference and polynomial, and their mathematical expectation was found, as well as variance for the difference characteristic. This allows us to make a conclusion about its quality by comparing the result of calculating the characteristic for a particular substitution with the calculated mathematical expectation. From a computational point of view, the thesises of the article are of exceptional interest due to the simplicity of the algorithm for quantifying the quality of bijective function substitutions. By its nature, the operation of calculating the difference characteristic carries out a simple summation of integer terms in a fixed and small range. Such an operation, both in the modern and in the prospective element base, is embedded in the logic of a wide range of functional elements, especially when implementing computational actions in the optical range, or on other carriers related to the field of nanotechnology.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Synthesis, Characterization and Antiproliferative Activity Assessment of a Novel 1H-5-mercapto-1,2,4 Triazole Derivative

2017 ◽  
Vol 68 (4) ◽  
pp. 745-747 ◽  
Author(s):  
Marius Mioc ◽  
Sorin Avram ◽  
Vasile Bercean ◽  
Mihaela Balan Porcarasu ◽  
Codruta Soica ◽  
...  
Keyword(s):  
Antiproliferative Activity ◽  
Biological Activities ◽  
Cancer Cell Line ◽  
Vascular Endothelial ◽  
Triazole Derivative ◽  
Activity Assessment ◽  
Wide Range ◽  
Specific Receptors ◽  
Endothelial Growth Factor ◽  
Selection Of

Angiogenesis plays an important function in tumor proliferation, one of the main angiogenic promoters being the vascular endothelial growth factor (VEGF) which activates specific receptors, particularly VEGFR-2. Thus, VEGFR-2 has become an essential therapeutic target in the development of new antitumor drugs. 1,2,4-triazoles show a wide range of biological activities, including antitumor effect, which was documented by numerous reports. In the current study the selection of 5-mercapto-1,2,4-triazole structure (1H-3-styryl-5-benzylidenehydrazino-carbonyl-methylsulfanil-1,2,4-triazole, Tz3a.7) was conducted based on molecular docking that emphasized it as suitable ligand for VEGFR-2 and EGFR1 receptors. Compound Tz3a.7 was synthesized and physicochemically and biologically evaluated thus revealing a moderate antiproliferative activity against breast cancer cell line MDA-MB-231.

1,4-Diazepines: A Review on Synthesis, Reactions and Biological Significance

2019 ◽  
Vol 16 (5) ◽  
pp. 709-729 ◽  
Author(s):  
Muhammad A. Rashid ◽  
Aisha Ashraf ◽  
Sahibzada S. Rehman ◽  
Shaukat A. Shahid ◽  
Adeel Mahmood ◽  
...  
Keyword(s):  
Biological Activities ◽  
Biological Significance ◽  
Biological Evaluation ◽  
Wide Range ◽  
Pharmaceutical Industries ◽  
Biological Aspects ◽  
Synthetic Routes ◽  
Biological Attributes ◽  
Synthesis Reactions ◽  
Potential Use

Background:1,4-Diazepines are two nitrogen containing seven membered heterocyclic compounds and associated with a wide range of biological activities. Due to its medicinal importance, scientists are actively involved in the synthesis, reactions and biological evaluation of 1,4-diazepines since number of decades.Objective:The primary purpose of this review is to discuss the synthetic schemes and reactivity of 1,4- diazepines. This article also describes biological aspects of 1,4-diazepine derivatives, that can be usefully exploited for the pharmaceutical sector.Conclusion:This review summarizes the abundant literature on synthetic routes, chemical reactions and biological attributes of 1,4-diazepine derivatives. We concluded that 1,4-diazepines have significant importance due to their biological activities like antipsychotic, anxiolytic, anthelmintic, anticonvulsant, antibacterial, antifungal and anticancer. 1,4-diazepine derivatives with significant biological activities could be explored for potential use in the pharmaceutical industries.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

Effect of Contact Ratio on Spur Gear Dynamic Load With No Tooth Profile Modifications

1996 ◽  
Vol 118 (3) ◽  
pp. 439-443 ◽  
Author(s):  
Chuen-Huei Liou ◽  
Hsiang Hsi Lin ◽  
F. B. Oswald ◽  
D. P. Townsend
Keyword(s):  
Dynamic Load ◽  
Spur Gear ◽  
Tooth Size ◽  
Contact Ratio ◽  
Gear Dynamics ◽  
Wide Range ◽  
Gear Contact ◽  
Center Distance ◽  
Selection Of ◽  
Better Than

This paper presents a computer simulation showing how the gear contact ratio affects the dynamic load on a spur gear transmission. The contact ratio can be affected by the tooth addendum, the pressure angle, the tooth size (diametral pitch), and the center distance. The analysis presented in this paper was performed by using the NASA gear dynamics code DANST. In the analysis, the contact ratio was varied over the range 1.20 to 2.40 by changing the length of the tooth addendum. In order to simplify the analysis, other parameters related to contact ratio were held constant. The contact ratio was found to have a significant influence on gear dynamics. Over a wide range of operating speeds, a contact ratio close to 2.0 minimized dynamic load. For low-contact-ratio gears (contact ratio less than two), increasing the contact ratio reduced gear dynamic load. For high-contact-ratio gears (contact ratio equal to or greater than 2.0), the selection of contact ratio should take into consideration the intended operating speeds. In general, high-contact-ratio gears minimized dynamic load better than low-contact-ratio gears.

scholarly journals A Virtual Telescope for the Open University Science Foundation Course

1998 ◽  
Vol 162 ◽  
pp. 100-105
Author(s):  
Andrew J. Norton ◽  
Mark H. Jones
Keyword(s):  
Social Science ◽  
Open University ◽  
Distance Teaching ◽  
The Arts ◽  
Wide Range ◽  
University Science ◽  
First Time ◽  
Degree Level ◽  
Selection Of

The Open University is the UK's foremost distance teaching university. For over twenty five years we have been presenting courses to students spanning a wide range of degree level and vocational subjects. Since we have no pre-requisites for entry, a major component of our course profile is a selection of foundation courses comprising one each in the Arts, Social Science, Mathematics, Technology and Science faculties. The Science Faculty's foundation course is currently undergoing a substantial revision. The new course, entitled “S103: Discovering Science”, will be presented to students for the first time in 1998.

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