Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools

Antiviral peptides (AVPs) represent a novel and promising therapeutic alternative to conventional antiviral treatments, due to their broad-spectrum activity, high specificity, and low toxicity. The emergence of zoonotic viruses such as Zika, Ebola, and SARS-CoV-2 have accelerated AVP research, driven by advancements in data availability and artificial intelligence (AI). This review focuses on the development of AVP databases, their physicochemical properties, and predictive tools utilizing machine learning for AVP discovery. Machine learning plays a pivotal role in advancing and developing antiviral peptides and peptidomimetics, particularly through the development of specialized databases such as DRAVP, AVPdb, and DBAASP. These resources facilitate AVP characterization but face limitations, including small datasets, incomplete annotations, and inadequate integration with multi-omics data.The antiviral efficacy of AVPs is closely linked to their physicochemical properties, such as hydrophobicity and amphipathic α-helical structures, which enable viral membrane disruption and specific target interactions. Computational prediction tools employing machine learning and deep learning have significantly advanced AVP discovery. However, challenges like overfitting, limited experimental validation, and a lack of mechanistic insights hinder clinical translation.Future advancements should focus on improved validation frameworks, integration of in vivo data, and the development of interpretable models to elucidate AVP mechanisms. Expanding predictive models to address multi-target interactions and incorporating complex biological environments will be crucial for translating AVPs into effective clinical therapies.