ABSTRACT
Background The ongoing COVID-19 pandemic has caused global economic crisis and dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides candidates.Methods In this study, we integrated several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pretrained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset.Results The highest accuracy of ACP-Dnnel reaches 98%, and the MCC value exceeds 0.9. On three different datasets, its average accuracy is 96.33%. After the latest independent data set validation, ACP-Dnnel improved at MCC, Sn and ACC values by 10.1%, 16.4% and 7.3% respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides prediction and it is available at http://150.158.148.228:5000/.