Prediction of chemical compounds properties using a deep learning model

Galushka, Mykola; Swain, Chris; Browne, Fiona; Mulvenna, Maurice; Bond, Raymond; Gray, Darren J.

doi:10.1007/s00521-021-05961-4

Cited by 20 publications

(16 citation statements)

References 40 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the uncertainty associated with the formal statistical analysis, the study adopted the contemporary and advanced machine learning technique to understand the regressors’ interactions and data prediction. , Of the popular algorithms, the DNN showed effectiveness in solving numerous regression and classification problems by capturing the patterns from the data and performing nonlinear transformations. , The model functions in a style that applies the nonlinearity at each hidden layer to obtain the abstract representation . The study employed the feedforward neural network model to predict the fructose yield, fructose selectivity, and glucose conversion responses generated by the impact of the varied MgO concentrations on CaO at different time and temperature intervals.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the materials (MgO and CaO) belonging to the same group of elements (s-block) and possessing identical chemical nature, except OH formation, can exhibit a uniform distribution of the cationic species in the nanocomposite, increasing the expectation of the catalyst’s quality in terms of the reaction performance (enriched product synthesis with a higher carbon efficiency) based on the placement of the individual components that offer a proportionated basicity. In addition, the determination of the influence of the nanocomposite on fructose synthesis under varied reaction severities and extrapolation of the results were carried out by employing the contemporary machine learning technique, that is, the cutting-edge deep neural network (DNN) model. , In recent years, the application of artificial intelligence in chemical catalysis and bioprocess systems for data prediction, control, and optimization on the widely diverse, unstructured, and interconnected data sets is becoming popular to better understand the processes. , Moreover, the study performed the technoeconomic analysis to verify the process’s feasibility and product’s cost-competitiveness.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MgO/CaO Nanocomposite Facilitates Economical Production of d-Fructose and d-Allulose Using Glucose and Its Response Prediction Using a DNN Model

Arumugam¹,

Singh

Mahala

et al. 2022

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

This study presents a method for the economical production of fructose and allulose (a valuable byproduct) directly from glucose over a MgO/CaO nanocomposite under an aqueous condition. The catalyst containing MgO and CaO at equal proportions helped manipulate the inherent characteristics of CaO, particularly strong basicity and surface properties. The analytical characterizations revealed that the structural assembly is such that MgO settles at the surface to initiate the isomerization reaction by providing a higher number of weak/medium base sites. The CaO present beneath undertakes the sequential conversion of the enolintermediate to ultimate fructose and byproducts (mannose and allulose). Thus, the catalyst accelerated the glucose interconversion to obtain a fructose yield as high as 33 wt % with 80% selectivity within 15 min. At the same time, it also initiated the C-3 fructose epimerization to yield allulose (a low-calorie sugar molecule). Moreover, the adopted deep neural network modeling well predicted the catalytic response with the MAE <5%. The technoeconomic analysis estimated the minimum selling price of different products to be US $ ∼4/kg (fructose), $ ∼4/kg (mannose), and $ ∼10/kg (allulose).

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MgO/CaO Nanocomposite Facilitates Economical Production of d-Fructose and d-Allulose Using Glucose and Its Response Prediction Using a DNN Model

Arumugam¹,

Singh

Mahala

et al. 2022

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…Solutions to various problems using neural networks involve the following steps [4] A study of the applicability of deep learning models for classification tasks was considered in [5], and clarification for practical tasks related, for example, to chemistry was obtained to optimize predictions of chemical patterns in [6,7], [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…where: a1the first mixed starting moment, which is calculated using the formula (4); a2the second starting moment, which is calculated by the formula (5); mErrormathematical expectation for an error, which is calculated by the formula (6); mwmathematical expectation for synapses, which is calculated using the formula (7).…”

Section: "Seasonal Linear Regression" Modelmentioning

confidence: 99%

Accelerating the learning process of a neural network by predicting the weight coefficient

Speranskyy¹,

Domanciuc²

2021

HAIT

View full text Add to dashboard Cite

The purpose of this study is to analyze and implement the acceleration of the neural network learning process by predicting the weight coefficients. The relevance of accelerating the learning of neural networks is touched upon, as well as the possibility of using prediction models in a wide range of tasks where it is necessary to build fast classifiers. When data is received from the array of sensors of a chemical unit in real time, it is necessary to be able to predict changes and change the operating parameters. After assessment, this should be done as quickly as possible in order to promptly change the current structure and state of the resulting substances.. Work on speeding up classifiers usually focuses on speeding up the applied classifier. The calculation of the predicted values of the weight coefficients is carried out using the calculation of the value using the known prediction models. The possibility of the combined use of prediction models and optimization models was tested to accelerate the learning process of a neural network. The scientific novelty of the study lies in the effectiveness analysis of prediction models use in training neural networks. For the experimental evaluation of the effectiveness of prediction models use, the classification problem was chosen. To solve the experimental problem, the type of neural network “multilayer perceptron” was chosen. The experiment is divided into several stages: initial training of the neural network without a model, and then using prediction models; initial training of a neural network without an optimization method, and then using optimization methods; initial training of the neural network using combinations of prediction models and optimization methods; measuring the relative error of using prediction models, optimization methods and combined use. Models such as “Seasonal Linear Regression”, “Simple Moving Average”, and “Jump” were used in the experiment. The “Jump” model was proposed and developed based on the results of observing the dependence of changes in the values of the weighting coefficient on the epoch. Methods such as “Adagrad”, “Adadelta”, “Adam” were chosen for training neural and subsequent verification of the combined use of prediction models with optimization methods. As a result of the study, the effectiveness of the use of prediction models in predicting the weight coefficients of a neural network has been revealed. The idea is proposed and models are used that can significantly reduce the training time of a neural network. The idea of using prediction models is that the model of the change in the weight coefficient from the epoch is a time series, which in turn tends to a certain value. As a result of the study, it was found that it is possible to combine prediction models and optimization models. Also, prediction models do not interfere with optimization models, since they do not affect the formula of the training itself, as a result of which it is possible to achieve rapid training of the neural network. In the practical part of the work, two known prediction models and the proposed developed model were used. As a result of the experiment, operating conditions were determined using prediction models.

show abstract

“…Recently, there has been a growing enthusiasm for using deep learning to advance drug discovery. − Deep learning has been successfully applied in compound property prediction, , de novo design, − lead discovery, repurposing, − and synthetic design . Deep learning models demonstrate significant improvements in rapidly screening potent leads from massive compounds in available compound libraries.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Promotes the Screening of Natural Products with Potential Microtubule Inhibition Activity

et al. 2022

View full text Add to dashboard Cite

Natural microtubule inhibitors, such as paclitaxel and ixabepilone, are key sources of novel medications, which have a considerable influence on anti-tumor chemotherapy. Natural product chemists have been encouraged to create novel methodologies for screening the new generation of microtubule inhibitors from the enormous natural product library. There have been major advancements in the use of artificial intelligence in medication discovery recently. Deep learning algorithms, in particular, have shown promise in terms of swiftly screening effective leads from huge compound libraries and producing novel compounds with desirable features. We used a deep neural network to search for potent β-microtubule inhibitors in natural goods. Eleutherobin, bruceine D (BD), and phorbol 12-myristate 13-acetate (PMA) are three highly effective natural compounds that have been found as β-microtubule inhibitors. In conclusion, this paper describes the use of deep learning to screen for effective β-microtubule inhibitors. This research also demonstrates the promising possibility of employing deep learning to develop drugs from natural products for a wider range of disorders.

show abstract

Prediction of chemical compounds properties using a deep learning model

Cited by 20 publications

References 40 publications

MgO/CaO Nanocomposite Facilitates Economical Production of d-Fructose and d-Allulose Using Glucose and Its Response Prediction Using a DNN Model

MgO/CaO Nanocomposite Facilitates Economical Production of d-Fructose and d-Allulose Using Glucose and Its Response Prediction Using a DNN Model

Accelerating the learning process of a neural network by predicting the weight coefficient

Deep Learning Promotes the Screening of Natural Products with Potential Microtubule Inhibition Activity

Contact Info

Product

Resources

About