2018/10/17 by Payel Das, Kahini Wadhawan, Das, Payel +18 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Immunology and Microbiology · Mathematics · #Antimicrobial #Antimicrobial Peptides and Activities #Antimicrobial peptides #Artificial intelligence #Autoencoder #Biochemical and Structural Characterization #Biochemistry #Biology #Classifier (UML) #Computational biology #Computer science #Context (archaeology) #Deep learning #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Microbiology #Peptide #Quantitative Methods (q-bio.QM) #cs.LG #q-bio.QM #stat.ML #vaccines and immunoinformatics approaches
paper · pdf · doi:10.48550/arxiv.1810.07743
openalex publication_date 2018/10/17 · arxiv created 2018/11/13 · arxiv updated 2018/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Given the emerging global threat of antimicrobial resistance, new methods for next-generation antimicrobial design are urgently needed. We report a peptide generation framework PepCVAE, based on a semi-supervised variational autoencoder (VAE) model, for designing novel antimicrobial peptide (AMP) sequences. Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences. The model further learns a disentangled antimicrobial attribute space by using the feedback from a jointly trained AMP classifier that uses limited labeled instances. The disentangled representation allows for controllable generation of AMPs. Extensive analysis of the PepCVAE-generated sequences reveals superior performance of our model in comparison to a plain VAE, as PepCVAE generates novel AMP sequences with higher long-range diversity, while being closer to the training distribution of biological peptides. These features are highly desired in next-generation antimicrobial design.