DEEPSIDE A DEEP LEARNING FRAMEWORK FOR DRUG SIDE EFFECT PREDICTION

Abstract

Drug failures brought on by unanticipated side effects during clinical trials put participants' health at risk and result in significant financial losses. Algorithms for predicting side effects may direct the development of new drugs. The LINCS L1000 dataset establishes a knowledge foundation for context-specific characteristics and offers a wealth of cell line gene expression data that has been altered by various medications. The state-of-the-art method, which seeks to use context-specific information, discards a significant number of the trials and only uses the high-quality experiments in LINCS L1000. Our aim in this research is to maximize the prediction performance by fully using this data. Five deep learning architectures are used in our experiments. We discover that when drug chemical structure (CS) and the whole collection of drug altered gene expression profiles (GEX) are employed as modalities, a multi-modal architecture yields the greatest prediction performance among multi-layer perceptron-based designs. We find that overall, the CS provides more information than the GEX. The best results are obtained by a convolutional neural network-based model that just employs the SMILES string representation of the medications; this model outperforms the state-of-the-art by 3:1% micro-AUC and 13:0% macro-AUC. Additionally, we demonstrate that the model can forecast drug-side effect couples that are absent from the ground truth side effect dataset but are described in the literature.