MLCNN-LSTM-COV: A Multilabel Convolutional Neural Network and Long Short-Term Memory-Based Framework for Predicting Adverse COVID Drug Reactions

Due to the worldwide COVID-19 pandemic, developing a specific drug to combat the Coronavirus Disease (COVID) has been essential. However, pinpointing potential adverse reactions to these drugs pose challenges in the quest for efficacious COVID treatment. Recent improvements in computational models employed in pharmaceutical production have opened novel avenues for detecting such adverse reactions. In response to the pressing demand for developing effective COVID drugs, this research introduces a methodology that combines a Multi-label Convolutional Neural Network and Long Short-Term Memory (MLCNN-LSTM-COV). The experimental evaluations here use COVID drug ’ball and stick’ chemical conformers. The distinctive features of the chemical conformers are represented through the RGB color channel, and features are extracted by employing a convolutional neural network, MaxPooling2D, and Long Short-Term Memory (LSTM) layers. The results showcase the superior efficiency of the MLCNN-LSTM-COV model, surpassing the performance of the previous study. Notably, the proposed model achieves the highest accuracy of 95.33% and a co-occurrence adverse drug reaction detection rate of 78.12%. This research represents a notable advancement in modeling Adverse Drug Reactions (ADR), especially in COVID treatment.