The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Deep learning improves antimicrobial peptide recognition
Open accessAbstract
Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. Results: In this work, we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive dataset. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino acid types. Availability and implementation: Models and datasets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at www.ampscanner.com. Supplementary information: Supplementary data are available at Bioinformatics online.
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UniKP: a unified framework for the prediction of enzyme kinetic parameters
Yu, H., Deng, H., He, J., Keasling, J. D., & Luo, X. (2023) · Nature Communications · doi:10.1038/s41467-023-44113-1 · Cited by 226
Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning
Dong, R., Cao, Q., & Song, C. (2025) · Advanced Science · doi:10.1002/advs.202506332 · Cited by 11
Plant-associated fungi co-opt ancient antimicrobials for host manipulation
Mesny, F., Wolf, V., López-Moral, A., Kraege, A., Punt, W., Liu, S., Zhu, J., Park, J., Sato, Y., & Thomma, B. P. H. J. (2026) · Science Advances · doi:10.1126/sciadv.aec1406 · Cited by 11
Elder, L. H., & Onufriev, A. V. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c02263 · Cited by 2
Metadata
- Authors
- Veltri, D., Kamath, U., & Shehu, A.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-03-24
- Volume
- 34
- Issue
- 16
- Pages
- 2740-2747
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2791848964
- Cited by
- 593
- References
- 6
- FWCI
- 22.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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