ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks

Abstract

Anticancer peptide (ACP) is a class of anti-cancer peptide which can inhibit and kill tumor cells. Identification of ACPs is of great significance for the development of new anti-cancer drugs. However, most of computational methods make predictions based on machine learning using hand-crafted features. In this article, we propose a new graph learning based computational model, named ACP-GCN, to automatically and accurately predict ACPs based on graph convolution networks. In this model, we for the first time take the ACP prediction as a graph classification task, where each peptide sample is represented as a graph. The experimental results show that the proposed model outperforms most of state-of-the-art methods, demonstrating that the proposed method can effectively distinguish ACPs from non-ACPs. The excellent predictive ability will rapidly push forward their applications in cancer therapy.

Keywords

• Anti-cancer peptides
• graph convolution networks
• machine learning
• prediction methods