SPP-CPI: Predicting Compound–Protein Interactions Based On Neural Networks

Abstract

Identifying interactions between compound and protein is a substantial part of the drug discovery process. Accurate prediction of interaction relationships can greatly reduce the time of drug development. The uniqueness of our method lies in three aspects:1) it represents a compound with a distance matrix. A distance matrix can capture the structural information, compared with the SMILES string. On the other hand, a distance matrix does not require complex data preprocessing for the molecular structure as the molecular graph representation, and is easier to obtain; 2) it uses SPP(Spatial pyramid pooling)-net to extract compound features, which has been successfully applied in image classification; and 3) it extracts protein features through the natural language processing method (doc2vec) to obtain sequence semantic information. We evaluated our method on three benchmark datasets-human, C.elegans, and DUDE-and the experimental results demonstrate that our proposed model presents competitive performance against state-of-the-art predictors. We also carried out drug-drug interaction (DDI) experiments to verify the strong potential of distance matrix as molecular characteristics. The source code and datasets are available at https://github.com/lxlsu/SPP_CPI.