Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning
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Abstract
In the post‐antibiotic era, antimicrobial peptides (AMPs) are considered ideal drug candidates because of their lower likelihood of inducing resistance. Computational models provide an efficient way to design novel AMPs. However, current optimization and generation approaches are tailored for specific application scenarios, which hinders the ease of use. To address this challenge, a novel AMP design model named AMPainter is proposed. Based on deep reinforcement learning, AMPainter integrates optimization and generation tasks in a unified framework. AMPainter is applied to three types of peptides, including known AMPs, signal peptides (SPs), and random sequences. AMPainter outperforms ten related models in enhancing the activity of known AMPs on the predicted antimicrobial potency and diversity. Several AMPs demonstrate a 128‐fold decrease in their actual minimal inhibitory concentrations (MICs). AMPainter evolves effective AMPs from membrane‐active SPs with an experimental success rate of 80%. In terms of generation, de novo designed AMP from an inactive random sequence achieves an average MIC of 2.88 µM against four bacteria. In vitro MICs of peptides along the virtual evolutionary path match the predicted scores. Therefore, AMPainter can significantly improve the antimicrobial potency of various peptides, expand the AMP sequence space, and discover novel antimicrobial agents.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015) · SoftwareX · doi:10.1016/j.softx.2015.06.001 · Cited by 27,190 · see citing works
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis
Murray, C. J. L., Ikuta, K. S., Sharara, F., Swetschinski, L., Robles Aguilar, G., Gray, A., Han, C., Bisignano, C., Rao, P., Wool, E., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(21)02724-0 · Cited by 16,316 · see citing works
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 7,936 · see citing works
CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins
Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., & MacKerell, A. (2016) · Nature Methods · doi:10.1038/nmeth.4067 · Cited by 6,708 · see citing works
Wiegand, I., Hilpert, K., & Hancock, R. E. W. (2008) · Nature Protocols · doi:10.1038/nprot.2007.521 · Cited by 6,156 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
MDAnalysis: A toolkit for the analysis of molecular dynamics simulations
Michaud‐Agrawal, N., Denning, E. J., Woolf, T. B., & Beckstein, O. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21787 · Cited by 4,549 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works
SignalP 6.0 predicts all five types of signal peptides using protein language models
Teufel, F., Armenteros, J. J. A., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, K. D., Winther, O., Brunak, S., von HEIJNE, G., & Nielsen, H. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01156-3 · Cited by 2,999 · see citing works
CHARMM-GUI Membrane Builder Toward Realistic Biological Membrane Simulations
Wu, E., Cheng, X., Jo, S., Rui, H., Song, K., Dávila-Contreras, E. M., Qi, Y., Lee, J.-M., Monje-Galvan, V., Venable, R., et al. (2014) · Journal of Computational Chemistry · doi:10.1002/jcc.23702 · Cited by 2,563 · see citing works
APD3: the antimicrobial peptide database as a tool for research and education
Wang, G., Li, X., & Wang, Z. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1278 · Cited by 2,402 · see citing works
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329 · see citing works
Antimicrobial Peptides: An Emerging Category of Therapeutic Agents
Mahlapuu, M., Håkansson, J., Ringstad, L., & Björn, C. (2016) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2016.00194 · Cited by 1,940 · see citing works
SignalP 6.0 predicts all five types of signal peptides using protein language models
Teufel, F., Armenteros, J. J. A., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, K. D., Winther, O., Brunak, S., von HEIJNE, G., & Nielsen, H. (2022) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000524414 · Cited by 1,905 · see citing works
Feng, Y., You, H., Zhang, Z., Ji, R., & Gao, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013558 · Cited by 1,895 · see citing works
Antimicrobial host defence peptides: functions and clinical potential
Mookherjee, N., Anderson, M. A., Haagsman, H. P., & Davidson, D. J. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0058-8 · Cited by 1,502 · see citing works
Machine-learning-guided directed evolution for protein engineering
Yang, K., Wu, Z., & Arnold, F. H. (2019) · Nature Methods · doi:10.1038/s41592-019-0496-6 · Cited by 1,085 · see citing works
Clustering of highly homologous sequences to reduce the size of large protein databases
Li, W.-Z., Jaroszewski, L., & Godzik, A. (2001) · Bioinform. · doi:10.1093/bioinformatics/17.3.282 · Cited by 1,021 · see citing works
Pirtskhalava, M., Amstrong, A. A., Grigolava, M., Chubinidze, M., Alimbarashvili, E., Vishnepolsky, B., Gabrielian, A. E., Rosenthal, A., Hurt, D. E., & Tartakovsky, M. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa991 · Cited by 725 · see citing works
Functional inference integrated in the FROGS suite
Samb, M., Bernard, M., & Pascal, G. (2020) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.1038/s41587 · Cited by 631 · see citing works
Deep learning improves antimicrobial peptide recognition
Veltri, D., Kamath, U., & Shehu, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty179 · Cited by 607 · see citing works
DeepPurpose: a deep learning library for drug–target interaction prediction
Huang, K., Fu, T., Glass, L. M., Žitnik, M., Xiao, C., & Sun, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1005 · Cited by 493 · see citing works
Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations
Das, P., Sercu, T., Wadhawan, K., Padhi, I., Gehrmann, S., Cipcigan, F. S., Chenthamarakshan, V., Strobelt, H., dos Santos, C., Chen, P.-Y., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00689-x · Cited by 481 · see citing works
Discovery of antimicrobial peptides in the global microbiome with machine learning
Júnior, C. D. S., Torres, M. D. T., Duan, Y., del Río, Á. R., Schmidt, T., Chong, H., Fullam, A., Kuhn, M., Zhu, C., Houseman, A., et al. (2024) · Cell · doi:10.1016/j.cell.2024.05.013 · Cited by 435 · see citing works
Low-N protein engineering with data-efficient deep learning
Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M., & Church, G. M. (2021) · Nature Methods · doi:10.1038/s41592-021-01100-y · Cited by 422 · see citing works
On Bayesian Methods for Seeking the Extremum
Mockus, A. (1975) · Springer eBooks · doi:10.1007/978-3-662-38527-2_55 · Cited by 410 · see citing works
The CMA Evolution Strategy: A Comparing Review
Hansen, N. (2006) · Springer eBooks · doi:10.1007/11007937_4 · Cited by 323 · see citing works
Porto, W. F., Irazazabal, L. N., Alves, E. S. F., Ribeiro, S. M., Matos, C. O., Pires, Á. S., Fensterseifer, I. C. M., Miranda, V. D. J., Haney, E. F., Humblot, V., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03746-3 · Cited by 286 · see citing works
CAMPR4: a database of natural and synthetic antimicrobial peptides
Gawde, U., Chakraborty, S., Waghu, F. H., Barai, R. S., Khanderkar, A., Indraguru, R., Shirsat, T., & Idicula‐Thomas, S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac933 · Cited by 282 · see citing works
Huang, J., Xu, Y., Xue, Y., Huang, Y., Li, X., Chen, X., Xu, Y., Zhang, D., Zhang, P., Zhao, J., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00991-2 · Cited by 276 · see citing works
Antimicrobial peptides: An alternative to traditional antibiotics
Ji, S., An, F., Zhang, T., Lou, M., Guo, J., Liu, K., Zhu, Y., Wu, J., & Wu, R. (2023) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2023.116072 · Cited by 240 · see citing works
Yan, K., Lv, H., Guo, Y., Peng, W., & Liu, B. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac715 · Cited by 233 · see citing works
Discovering highly potent antimicrobial peptides with deep generative model HydrAMP
Szymczak, P., Możejko, M., Grzegorzek, T., Jurczak, R., Bauer, M., Neubauer, D., Sikora, K., Michalski, M., Sroka, J., Setny, P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36994-z · Cited by 231 · see citing works
Ankh ☥: Optimized Protein Language Model Unlocks General-Purpose ModellingPreprint
Elnaggar, A., Essam, H., Salah-Eldin, W., Moustafa, W., Elkerdawy, M., Rochereau, C., & Rost, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.16.524265 · Cited by 201 · see citing works
Mining for encrypted peptide antibiotics in the human proteome
Torres, M. D. T., Melo, M. C. R., Flowers, L., Crescenzi, O., Notomista, E., & de la Fuente‐Núñez, C. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00801-1 · Cited by 196 · see citing works
Pandi, A., Adam, D., Zare, A., Trinh, V. T., Schaefer, S. L., Burt, M., Klabunde, B., Bobkova, E., Kushwaha, M., Foroughijabbari, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42434-9 · Cited by 173 · see citing works
Mining human microbiomes reveals an untapped source of peptide antibiotics
Torres, M. D. T., Brooks, E. F., Cesaro, A., Sberro, H., Gill, M. O., Nicolaou, C., Bhatt, A. S., & de la Fuente‐Núñez, C. (2024) · Cell · doi:10.1016/j.cell.2024.07.027 · Cited by 172 · see citing works
Yoshida, M., Hinkley, T., Tsuda, S., Abul‐Haija, Y. M., McBurney, R. T., Kulikov, V., Mathieson, J. S., Reyes, S. G., de Castro, M. D. L., & Cronin, L. (2018) · Chem · doi:10.1016/j.chempr.2018.01.005 · Cited by 168 · see citing works
AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides
Oort, C. M. V., Ferrell, J. B., Remington, J. M., Wshah, S., & Li, J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01441 · Cited by 168 · see citing works
Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning
Maasch, J. R. M. A., Torres, M. D. T., Melo, M. C. R., & de la Fuente‐Núñez, C. (2023) · Cell Host & Microbe · doi:10.1016/j.chom.2023.07.001 · Cited by 156 · see citing works
Li, T., Ren, X., Luo, X., Wang, Z., Li, Z., Luo, X., Shen, J., Li, Y., Yuan, D., Nussinov, R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51933-2 · Cited by 143 · see citing works
Macrel: antimicrobial peptide screening in genomes and metagenomes
Júnior, C. D. S., Pan, S., Zhao, X., & Coelho, L. P. (2020) · PeerJ · doi:10.7717/peerj.10555 · Cited by 132 · see citing works
Dean, S. N., Alvarez, J. A. E., Zabetakis, D., Walper, S. A., & Malanoski, A. P. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.725727 · Cited by 128 · see citing works
Designing antimicrobial peptides using deep learning and molecular dynamic simulations
Cao, Q., Ge, C., Wang, X., Harvey, P. J., Zhang, Z., Ma, Y., Wang, X., Jia, X., Mobli, M., Craik, D. J., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad058 · Cited by 119 · see citing works
Self-citationDe novo design of self-assembling peptides with antimicrobial activity guided by deep learning
Liu, H., Song, Z., Zhang, Y. L., Wu, B., Chen, D., Zhou, Z., Zhang, H., Li, S., Feng, X., Huang, J., et al. (2025) · Nature Materials · doi:10.1038/s41563-025-02164-3 · Cited by 118 · see citing works
Ma, T., Liu, Y., Yu, B., Sun, X., Yao, H., Hao, C., Li, J., Nawaz, M., Jiang, X., Lao, X., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1046 · Cited by 117 · see citing works
Artificial intelligence-driven antimicrobial peptide discovery
Szymczak, P., & Szczurek, E. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102733 · Cited by 100 · see citing works
ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity
Pan, X., Zuallaert, J., Wang, X., Shen, H., Campos, E. P., Marushchak, D. O., & Neve, W. D. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa656 · Cited by 96 · see citing works
Wang, R., Wang, T., Zhuo, L., Wei, J., Fu, X., Zou, Q., & Yao, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae078 · Cited by 93 · see citing works
Wang, B., Lin, P., Zhong, Y., Tan, X., Shen, Y., Huang, Y., Jin, K., Zhang, Y., Zhan, Y., Shen, D., et al. (2025) · Nature Microbiology · doi:10.1038/s41564-024-01907-3 · Cited by 82 · see citing works
ampir: an R package for fast genome-wide prediction of antimicrobial peptides
Fingerhut, L. C. H. W., Miller, D. J., Strugnell, J. M., Daly, N. L., & Cooke, I. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa653 · Cited by 75 · see citing works
Self-play reinforcement learning guides protein engineering
Wang, Y., Tang, H., Huang, L., Pan, L., Yang, L., Yang, H., Mu, F., & Yang, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00691-9 · Cited by 69 · see citing works
Design of a Potent Antibiotic Peptide Based on the Active Region of Human Defensin 5
Wang, C., Shen, M., Gohain, N., Tolbert, W. D., Chen, F., Zhang, N., Yang, K., Wang, A., Su, Y., Cheng, T., et al. (2015) · Journal of Medicinal Chemistry · doi:10.1021/jm501824a · Cited by 53 · see citing works
Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders
van Hilten, N., Methorst, J., Verwei, N., & Risselada, H. J. (2023) · Science Advances · doi:10.1126/sciadv.ade8839 · Cited by 49 · see citing works
Randall, J. R., Vieira, L. C., Wilke, C. O., & Davies, B. W. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01243-1 · Cited by 46 · see citing works
Yan, J., Zhang, B., Zhou, M., Campbell‐Valois, F., & Siu, S. W. I. (2023) · mSystems · doi:10.1128/msystems.00345-23 · Cited by 40 · see citing works
Dong, R., Liu, R., Liu, Z., Liu, Y., Zhao, G., Li, H., Hou, S., Ma, X., Kang, H., Liu, J., et al. (2024) · eLife · doi:10.7554/elife.97330.3 · Cited by 30 · see citing works
Self-citationProximal Exploration for Model-guided Protein Sequence DesignPreprint
Ren, Z., Li, J., Ding, F., Zhou, Y., Ma, J., & Peng, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.12.487986 · Cited by 20 · see citing works
Fuse feeds as one: cross-modal framework for general identification of AMPs
Zhang, W., Xu, Y., Wang, A., Chen, G., & Zhao, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad336 · Cited by 19 · see citing works
Evolution-Based Protein Engineering for Antifungal Peptide Improvement
Gu, J., Isozumi, N., Yuan, S., Jin, L., Gao, B., Ohki, S., & Zhu, S. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab224 · Cited by 18 · see citing works
In silico evolution of nucleic acid-binding proteins from a nonfunctional scaffold
Raven, S. A., Payne, B., Bruce, M., Filipovska, A., & Rackham, O. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-00967-y · Cited by 9 · see citing works
B., J., Goos, G., Hartmanis, J., & Marchuk, G. I. (1976) · Mathematics of Computation · doi:10.2307/2005413 · Cited by 6 · see citing works
Dong, R., Liu, R., Liu, Z., Liu, Y., Zhao, G., Li, H., Hou, S., Ma, X., Kang, H., Liu, J., et al. (2024) · eLife · doi:10.7554/elife.97330.1 · Cited by 1 · see citing works
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Flagged first, then by citations
1 of the 67 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Yuan, R.-X., Ma, X.-Y., Lv, Y., & Si, H. (2026) · Current Issues in Molecular Biology · doi:10.3390/cimb48010062 · Cited by 7 · see citing works
Wang, Z., Li, X., Chen, H., Du, J., Huang, L., Xin, T., Ma, M., Zhu, W., Zheng, P., Nie, C., et al. (2025) · Journal of Materials Chemistry B · doi:10.1039/d5tb01872b · Cited by 6 · see citing works
Hassan, M., Abu-El-Ruz, R., & Zughaier, S. M. (2026) · Digital Health · doi:10.1177/20552076261443074 · Cited by 4 · see citing works
Dong, R., Awang, T., Cao, Q., Kang, K., Wang, L., Zhu, Z., & Song, C. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag453 · Cited by 1 · see citing works
Self-citationWu, K., Sang, Z., Zhang, G., & Pang, H. (2026) · Small · doi:10.1002/smll.75132 · Cited by 1 · see citing works
Kang, Z., Zhang, H., Zhou, Q., Liu, J., Zhou, K., Chen, P., Liu, B., & Ning, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.13.688364 · Cited by 1 · see citing works
Evolutionary Design of Membrane-Lytic Antimicrobial Peptides with Mixture of ExpertsPreprint
Dong, R., & Song, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.29.741660
Self-citationSaleem, N., El-Omar, E., Kumar, N., Willcox, M. D. P., & Jiang, X. (2026) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2026.1894311
Zhang, X., Deng, J., Cao, Y., Feng, X., Liu, Y., Fu, X., & Ren, M. (2026) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2026.108027
A Retrospective Benchmark of Quantitative Antimicrobial Peptide Activity Prediction Tools
Yue, J., Tang, Z., Li, X., Xu, J., Tan, H., Yang, Q., Liu, Z., & Wang, Y. (2026) · Small Methods · doi:10.1002/smtd.71137
Chiriac, V. M., Ivu, A.-A., Rusu, A. G., Ghilan, A., Serban, A., Croitoriu, A., Nita, L. E., & Chiriac, A. P. (2026) · Gels · doi:10.3390/gels12110964
- Authors
- Dong, R., Cao, Q., & Song, C.
- Affiliations
- Peking University (CN)
- Corresponding author
- Song, Chen
- Venue
- Advanced Science
- Publisher
- Wiley
- Published
- 2025-09-12
- Volume
- 12
- Issue
- 43
- Pages
- e06332-e06332
- ISSN
- 2198-3844 (ISSN-L)
- PMC
- PMC12631831
- OpenAlex
- W4414162912
- Cited by
- 11
- References
- 67
- Citations by year
- 2026: 9 · 2025: 2
- FWCI
- 5.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Advanced Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2024YFA0916800)
- Innovative Research Group Project of the National Natural Science Foundation of China (T2321001)
- Topics
- Life Sciences › Immunology and Microbiology › Microbiology › Antimicrobial Peptides and Activities
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- antimicrobial peptides, peptide design, deep reinforcement learning, signal peptide, minimum inhibitory concentration, antimicrobial potency, de novo peptide design, antibiotic resistance, post-antibiotic era
- MeSH terms
- Deep Learning, Antimicrobial Peptides, Reinforcement Machine Learning, Anti-Infective Agents, Anti-Bacterial Agents, Bacteria, Microbial Sensitivity Tests, Drug Design
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