Drug discovery and mechanism prediction with explainable graph neural networks.

Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. However, existing methods mainly focus on forward encoding of drugs, which is to obtain an accurate prediction of the response levels, but omitted to decipher the reaction mechanism between drug molecules and genes. We propose the eXplainable Graph-based Drug response Prediction (XGDP) approach that achieves a precise drug response prediction and reveals the comprehensive mechanism of action between drugs and their targets. XGDP represents drugs with molecular graphs, which naturally preserve the structural information of molecules and a Graph Neural Network module is applied to learn the latent features of molecules. Gene expression data from cancer cell lines are incorporated and processed by a Convolutional Neural Network module. A couple of deep learning attribution algorithms are leveraged to interpret interactions between drug molecular features and genes. We demonstrate that XGDP not only enhances the prediction accuracy compared to pioneering works but is also capable of capturing the salient functional groups of drugs and interactions with significant genes of cancer cells.