TrustworthyCPI: Trustworthy Compound–Protein Interaction Prediction

Motivation: Identifying Compound-Protein Interaction (CPI) plays an important role in the discovery and development of drugs. In contrast with traditional wet experiments which are time-consuming and expensive, computational approaches for CPI prediction are time-saving and cost-effective that are highly desired for us. However, the existing methods cannot provide confidence measure for prediction results which maybe risky for drug research and development. Results: We present a novel data-driven end-to-end learning-based method for trustworthy predicting compound-protein interactions (named TrustworthyCPI). The TrustworthyCPI has two main components: 1) Convolutional encoder of sequence operates directly on compound SMILEs and protein amino acid sequences to learn compound and protein latent representations respectively. 2) Evidence classifier not only predicts the compound-protein interaction probability but also yields the confidence level of predicted results as well. The parameters of two components are simultaneously trained by backpropagation in an end-to-end learning manner. The experimental results show that TrustworthyCPI achieves the prediction performance compared with the state-of-the-art CPI prediction methods in terms of ACC, AUC, and AUPRC, and has a good performance in the reliability measurement of predicted output. Additionally, case studies are provided for predicting interactions between existing drugs and SARS-CoV2 3C-Like Protease ($\rm {3CL}^{Pro}$), which further validates the effectiveness and feasibility of the proposed method.