iDVIP is a web server for identifying Viral integrase inhibitory peptides (VINIPs).

To facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed. In this study, we have developed the first model for identifying the novel VINIPs based on sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by five-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.85%, a specificity of 88.81%, an accuracy of 88.37%, and a Matthews Correlation Coefficient value of 0.64. Most importantly, the model also consistently provides the effective performance in independent testing.