Inceptionv3-LSTM-COV: A multi-label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short-term memory

Abstract

Due to the global COVID-19 pandemic, distinct medicines have been developed for treating the coronavirus disease (COVID). However, predicting and identifying potential adverse reactions to these medicines face significant challenges in producing effective COVID medication. Accurate prediction of adverse reactions to COVID medications is crucial for ensuring patient safety and medicine success. Recent advancements in computational models used in pharmaceutical production have opened up new possibilities for detecting such adverse reactions. Due to the urgent need for effective COVID medication development, this research presents a multi-label Inceptionv3 and long short-term memory methodology for COVID (Inceptionv3-LSTM-COV) medicine development. The presented experimental evaluations were conducted using the chemical conformer image of COVID medicine. The features of the chemical conformer are denoted utilizing the RGB color channel, which is extracted using Inceptionv3, GlobalAveragePooling2D, and long short-term memory (LSTM) layers. The results demonstrate that the efficiency of the Inceptionv3-LSTM-COV model outperformed the previous study's performance and achieved better results compared to MLCNN-COV, Inceptionv3, ResNet50, MobileNetv2, VGG19, and DenseNet201 models. The proposed model reported the highest accuracy value of 99.19% in predicting adverse reactions to COVID medicine.