Exploring target specificity of antimicrobial peptides through deep learning embeddings

In the face of increasing bacterial resistance to antibiotics, antimicrobial peptides (AMPs) have stood out as an encouraging target for the development of new drugs. Machine learning approaches can be applied to this area to characterize large sets of AMPs based on their bacterial targets, activity measures, and other sequence features. Such methods enable wet-laboratory researchers to optimize the speed and accuracy of their work by focusing on prioritized candidates [5].