Accelerating Prediction of Antiviral Peptides Using Genetic Algorithm-Based Weighted Multiperspective Descriptors with Self-Normalized Deep Networks

The accurate prediction of antiviral peptides (AVPs) plays a crucial role in accelerating the development of peptide-based therapeutics. Despite extensive production of antiviral medications, viral diseases remain a major human health concern. AVPs have emerged as potential candidates for the development of novel antiviral drugs. However, the available traditional methods are labor-intensive, expensive, and cannot provide a deeper structural and contextual understanding of the peptide sequences. To address these problems, we propose a novel deep computational model, TargetAVP-DeepCaps, for the precise prediction of AVPs. In this model, multiple innovative feature representation strategies were presented by encoding the input peptides using a pretrained ProtGPT2 model for contextual embeddings. On the other hand, sequence-to-image transformations are performed using SMR and RECM matrices. Additionally, the produced 2D images were locally decomposed using the CLBP approach to obtain the SMR-CLBP and RECM-CLBP descriptors. A differential evolution mechanism was applied to form a weighted-feature-based multiperspective vector. The optimal features were selected using a hybrid MRMD + SFLA feature selection approach. Finally, a novel self-normalized capsule network (Sn-CapsNet) model was developed to achieve a superior predictive accuracy of 97.36%, outperforming the available predictors by approximately 12% with an area under the curve (AUC) of 0.98. To ensure the generalization of the TargetAVP-DeepCaps model, our training achieved an approximately 8% higher prediction than previous models using an independent data set. The demonstrated effectiveness and robustness of TargetAVP-DeepCaps provide an advanced therapeutic tool for understanding peptide mechanisms and related applications in drug discovery.