Using deep 1D convolutional grated recurrent unit neural network to optimize quantum molecular properties and predict intramolecular coupling constants of molecules of potential health medications and other generic molecules
Article
Oyewola, D.O., Dada, E.G., Emebo, O. and Oluwagbemi, O. 2022. Using deep 1D convolutional grated recurrent unit neural network to optimize quantum molecular properties and predict intramolecular coupling constants of molecules of potential health medications and other generic molecules. Applied Sciences. 12 (14). https://doi.org/10.3390/app12147228
Type | Article |
---|---|
Title | Using deep 1D convolutional grated recurrent unit neural network to optimize quantum molecular properties and predict intramolecular coupling constants of molecules of potential health medications and other generic molecules |
Authors | Oyewola, D.O., Dada, E.G., Emebo, O. and Oluwagbemi, O. |
Abstract | A molecule is the smallest particle in a chemical element or compound that possesses the element or compound’s chemical characteristics. There are numerous challenges associated with the development of molecular simulations of fluid characteristics for industrial purposes. Fluid characteristics for industrial purposes find applications in the development of various liquid household products, such as liquid detergents, drinks, beverages, and liquid health medications, amongst others. Predicting the molecular properties of liquid pharmaceuticals or therapies to address health concerns is one of the greatest difficulties in drug development. Computational tools for precise prediction can help speed up and lower the cost of identifying new medications. A one-dimensional deep convolutional gated recurrent neural network (1D-CNN-GRU) was used in this study to offer a novel forecasting model for molecular property prediction of liquids or fluids. The signal data from molecular properties were pre-processed and normalized. A 1D convolutional neural network (1D-CNN) was then built to extract the characteristics of the normalized molecular property of the sequence data. Furthermore, gated recurrent unit (GRU) layers processed the extracted features to extract temporal features. The output features were then passed through several fully-connected layers for final prediction. For both training and validation, we used molecular properties obtained from the Kaggle database. The proposed method achieved a better prediction accuracy, with values of 0.0230, 0.1517, and 0.0693, respectively, in terms of the mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE). |
Keywords | deep learning; molecular property prediction; gated recurrent unit; convolutional neural network; convolutional gated recurrent unit neural network |
Sustainable Development Goals | 3 Good health and well-being |
Middlesex University Theme | Health & Wellbeing |
Publisher | MDPI |
Journal | Applied Sciences |
ISSN | |
Electronic | 2076-3417 |
Publication dates | |
Online | 18 Jul 2022 |
02 Jul 2022 | |
Publication process dates | |
Submitted | 22 May 2022 |
Accepted | 15 Jul 2022 |
Deposited | 11 Apr 2024 |
Output status | Published |
Publisher's version | License File Access Level Open |
Copyright Statement | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Digital Object Identifier (DOI) | https://doi.org/10.3390/app12147228 |
Web of Science identifier | WOS:000833807500001 |
Language | English |
https://repository.mdx.ac.uk/item/v2203
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