A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines.

ABSTRACT: An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of in silico tools being developed for the initial screening of therapeutic molecules. All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app , is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been develope

SUBMITTER: Singh V 

PROVIDER: S-EPMC10444765 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature