Abstract:
Rapid advances in machine learning have enabled the prediction of complex drug-drug interactions (DDIs) and associated
harmful effects. This study aims to develop a neural network model that can predict drug-drug interactions (DDIs) for various side
effects. Our study intends to create a reliable and easy-to-understand tool that can transform pharmaceutical research and healthcare
by lowering polypharmacy risks. Our method begins with the careful selection and compilation of large datasets on medicine
combinations, side effects, drug-side effect connections, and drug-protein interactions. We use an adjacency matrix to establish a
drug-protein network. Then, we use PCA to shrink the network. Using artificial neural networks, our neural network is designed for
binary categorization. This model is rigorously trained, validated, and tested using performance metrics to ensure its strength and
adaptability. Our model has remarkable accuracy, with AUC-ROC scores of 98.67% for certain interactions. Reading and handling
structured input is a major advantage of Artificial Neural Networks (ANNs) over Graph Convolutional Networks (GCNs). The
findings demonstrate our approach's versatility in pharmaceutical research and healthcare, including medication development and
real-time clinical decision help. To conclude, this study advances DDI prediction and management. Resilience, comprehensibility,
and accuracy make the model a flexible polypharmacy solution. By solving DDI prediction and side effect control, our strategy
might improve pharmaceutical research, patient safety, and healthcare results. This study shows how advanced machine learning may
be used in pharmaceuticals
