PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction

PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction

New methods for antimicrobial design are critical for combating pathogenic bacteria in the post-antibiotic era. Fortunately, competition within complex communities has led to the natural evolution of antimicrobial peptide (AMP) sequences that have promising bactericidal properties. Unfortunately, th...

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Bibliographic Details
Main Authors: Scott N. Dean, Jerome Anthony E. Alvarez, Dan Zabetakis, Scott A. Walper, Anthony P. Malanoski
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-09-01
Series:Frontiers in Microbiology
Subjects:
antimicrobial peptides
minimum inhibitory concentration
generative deep learning
activity prediction
variational autoencoder
Online Access:https://www.frontiersin.org/articles/10.3389/fmicb.2021.725727/full

Internet

https://www.frontiersin.org/articles/10.3389/fmicb.2021.725727/full

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