A Deep Learning Framework for Multi Drug Side Effects Prediction with Drug Chemical Substructure

Muhammad Asad Arshed, Shahzad Mumtaz, Omer Riaz, Waqas Sharif, Saima Abdullah
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Muhammad Asad Arshed, Shahzad Mumtaz, Omer Riaz, Waqas Sharif, Saima Abdullah: Department of Software Engineering, University of Management & Technology, Lahore, Pakistan. Department of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur, Pakistan. Department of Data Science, The Islamia University of Bahawalpur, Bahawalpur, Pakistan. Department of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan. Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

International Journal of Innovations in Science & Technology, 2022, vol. 4, issue 1, 19-31

Abstract: Nowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of multiple side effects of drugs with the chemical structure of drugs. As it is a common experience that a single drug can cause multiple side effects, that’s why we have proposed a deep learning model that can predict multiple side effects for a single drug. We have considered three side effects (Dizziness, Allergy, Headache) in this study. We have collected the drug side effects information from the SIDER database. We have achieved an accuracy of ‘0.9494’ with our multi-label classification based proposed model. The proposed model can be used in different stages of the drug development process

Keywords: Drug Side Effect; Drug Chemical Structure; Deep Learning; Multi-Label Classification; PCA (search for similar items in EconPapers)
Date: 2022
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