Descriptive prediction of drug side-effects using a hybrid deep learning model

In this study, we developed a hybrid deep learning (DL) model, which is one of the first interpretable hybrid DL models with Inception modules, to give a descriptive prediction of drug side-effects. The model consists of a graph convolutional neural network (GCNN) with Inception modules to allow more efficient learning of drug molecular features and bidirectional long short-term memory (BiLSTM) recurrent neural networks to associate drug structure with its associated side effects. The outputs from the two networks (GCNN and BiLSTM) are then concatenated and a fully connected network is used to predict the side effects of drugs. Our model achieves an AUC score of 0.846 irrespective of what classification threshold is chosen. It has a precision score of 0.925 and the Bilingual Evaluation Understudy (BLEU) scores obtained were 0.973, 0.938, 0.927, and 0.318 which show significant achievements despite the fact that a small drug data set is used for adverse drug reaction (ADR) prediction. Moreover, the model is capable of accurately structuring correct words to describe drug side-effects and associates them with its drug name and molecular structure. The predicted drug structure and ADR relation will provide a reference for preclinical safety pharmacology studies and facilitate the identification of ADRs during early phases of drug development. It can also help detect unknown ADRs embedded in existing drugs, hence contributing significantly to the science of pharmacovigilance.

Automated summary

In this study, a hybrid deep learning model utilizing graph convolutional neural network and bidirectional long short-term memory recurrent neural networks was developed to predict drug side-effects, achieving significant achievements in accuracy scores.