Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Quantitative Methods | Antimicrobial,Autoencoder,Peptide,Artificial intelligence,Classifier (linguistics),Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1810.07743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Payel Das | 1 | 43 | 12.59 |
| Kahini Wadhawan | 2 | 15 | 2.93 |
| Oscar Chang | 3 | 1 | 2.39 |
| Tom Sercu | 4 | 0 | 0.68 |
| Cícero Nogueira dos Santos | 5 | 771 | 37.83 |
| matthew riemer | 6 | 25 | 7.86 |
| Inkit Padhi | 7 | 46 | 6.29 |
| V. Chenthamarakshan | 8 | 114 | 12.11 |
| Aleksandra Mojsilovic | 9 | 288 | 39.15 |