A Separable Temporal Convolutional Networks Based Deep Learning Technique For Discovering Antiviral Medicines

This paper discusses the development of two in silico tools, Deep-AVPiden and Deep-AVPiden (DS), for the initial screening of therapeutic molecules. The models have an accuracy of 90% and 88%, respectively, and both have a precision of 90%. The models were compared to the state-of-the-art classifiers and found to be much better. Additionally, some AVPs were identified in the natural defense proteins of plants, mammals, and fishes and found to have appreciable sequence similarity with some experimentally validated antimicrobial peptides.