GraphCPI: Graph Neural Representation Learning for CompoundProtein Interaction Accurately predicting compoundprotein interactions (CPIs) is of great help to increase the efficiency and reduce costs in drug development. Most of existing machine learning models for CPI prediction often represent compounds and proteins in onedimensional strings, or use the descriptorbased methods. These models might ignore the fact that molecules are essentially structured by the chemical bond of atoms. However, in realworld scenarios, the topological structure information usually provides an overview of how the atoms are connected, and the local chemical context reveals the functionality of the protein sequence in CPI. These two types of information are complementary to each other and they are both important for modeling compounds and proteins. Motivated by this, this paper suggests an endtoend deep learning framework called GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences for solving the CPI prediction task. Our framework can integrate any popular graph nerual networks for learning compounds, and it combines with a convolutional neural network for embedding sequences. We conduct extensive experiments based on two benchmark CPI datasets. The experimental results demonstrate that our proposed framework is feasible and also competitive, comparing against classic and stateoftheart methods.

Integrating Sequence and Network Information to Enhance ProteinProtein Interaction Prediction Using Graph Convolutional Networks Identification of proteinprotein interactions (PPIs) is an important problem in biology, since PPIs are related to many essential cellular processes. The development of largescale highthroughput experiments has produced a large number of PPIs data, however, these data are often noisy and their coverage is still limited. To overcome the shortcomings of experimental methods, many computational methods have been proposed for the prediction of PPIs. Among these methods, most of them solely take the amino acid sequence of protein as input information to make predictions. As PPIs data form the PPIs networks graph, the position information of proteins in the graph can reflect the properties of proteins to some extent, which is an important complement to protein sequence information. But previous works did not consider the graph structure information to improve the prediction performance. In this work, we first time apply graph convolutional networks (GCNs) to capture the proteinu0027s position information in the graph and combine amino acid sequence information and position information to make representations in the prediction task. Our experimental results show that our work outperforms the stateoftheart sequencebased methods on several benchmark datasets and our work computationally is more efficient compared with previous works.