Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events

Abstract

Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs’ effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events. The DL model, i.e., Convolutional Neural Network (CNN), is used to analyze the DDI. DDI is based on the 65 different drug-associated events, which is present in the drug bank database. Our model uses the inputs, which are chemical structures (i.e., smiles of drugs), enzymes, pathways, and the target of the drug. Therefore, for the multi-model CNN, we use several layers, activation functions, and features of drugs to achieve better accuracy as compared to traditional prediction algorithms. We perform different experiments on various hyperparameters. We have also carried out experiments on various iterations of drug features in different sets. Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%. The results showed that a combination of the drug’s features (i.e., chemical substructure, target, and enzyme) performs better in DDIs-associated events prediction than other features.

Image-based molecular representation learning for drug development: a survey

“…In quantitative evaluations, the model can correctly reconstruct 97% of molecular images into structured formats and achieve an F1 score of about 97–98% in chemical entity recognition. MCNN-DDI [ 84 ] and MultiDTI [ 94 ] have achieved superior accuracy compared with traditional prediction algorithms by employing multi-modal training through inputs such as chemical structure (i.e. drug smiles), enzymes, pathways and drug targets.…”