Identify Unfavorable COVID Medicine Reactions from the Three-Dimensional Structure by Employing Convolutional Neural Network

Pranab Das and Dilwar Hussain Mazumder
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Pranab Das: National Institute of Technology Nagaland
Dilwar Hussain Mazumder: National Institute of Technology Nagaland

A chapter in Mathematical Modeling and Intelligent Control for Combating Pandemics, 2023, pp 155-167 from Springer

Abstract: Abstract The medicine development process is expensive, challenging, and time needed. Computational model-based classifiers have been employed to overcome these problems. One of the reasons for medicine failure is unfavorable reactions. So, it is prominent to identify unfavorable reactions during the medicine clinical testing phase with the help of computational models, such as convolutional neural network (CNN). Therefore, this chapter presents a CNN classifier that identifies the unfavorable COVID medicine reactions from the three-dimensional structure. Appropriately identifying unfavorable reactions of COVID medicine is vital in modern medicine development. To build the proposed CNN classifier, unfavorable medicine reactions are obtained from WebMD, and three-dimensional medicine structures are collected from PubChem. The presented CNN classifier in this chapter suggests that three-dimensional medicine structures are adequate to identify unfavorable reactions. The presented CNN model outperformed the pre-trained models’ performance and achieved an 87.16% accuracy score.

Keywords: COVID medicine reactions; Drug discovery; Chemical three-dimensional structure; Convolutional neural network (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-33183-1_9

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DOI: 10.1007/978-3-031-33183-1_9

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