Publication Date: 2022/08/16
Abstract: Drug function identification from the drug properties is important in drug discovery. Each year billions of dollars are spent on empirical testing of the drugs, which is costly, chemical wastage, and timeconsuming. The computational experiments would help reduce drug discovery time and cost significantly. Most of the existing works have focused on single-label drug function identification. However, the capability of the drug's biological properties (transporter, target, carrier, and enzyme) has not yet been explored for multiple drug function identification. Identifying drug function is a multi-label classification problem. So, in the present work, a multi-label long short-term memory-based framework has been proposed for identifying drug function. The data related to biological properties has been extracted from DrugBank, and drug functions are collected from PubChem. The proposed framework performance has been found promising in terms of accuracy, precision, recall, F1, ROC-AUC score, and hamming-loss, and it achieved the highest accuracy of 95.80%.
Keywords: Multi-Label, LSTM, Biological Properties, Drug Function, Machine Learning.
DOI: https://doi.org/10.5281/zenodo.6997140
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT22JUL448.pdf
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