Developability-Aware Artificial Intelligence for Antimicrobial Peptide Discovery: A Conceptual Framework Linking Activity Prediction to Stability, Aggregation and Delivery
DOI:
https://doi.org/10.68854/skjfga83Keywords:
antimicrobial peptides, AI-guided discovery, developability, proteolytic stability, aggregation, peptide formulation, machine learning, antimicrobial resistanceAbstract
Machine-learning and generative models now propose antimicrobial peptides with predicted and experimentally confirmed activity faster than any programme can characterise them, yet few candidates progress towards clinical use. The attrition is reported as pharmaceutical rather than microbiological, arising from proteolytic degradation, host-cell toxicity, poor solubility and aggregation, and the absence of a viable delivery route. This theoretical paper examines why activity-focused prediction has not been accompanied by comparable attention to pharmaceutical developability, and what follows for the decision to advance a candidate. The study is non-empirical and uses a conceptual research design. A corpus of peer-reviewed journal articles published between 2020 and 2026 was assembled across four literatures, namely computational antimicrobial peptide discovery, peptide proteolytic stability, peptide aggregation and solubility, and peptide formulation and delivery, with a supplementary search on antimicrobial resistance burden in sub-Saharan Africa. Every source was verified against its Crossref publisher record for authorship, journal, volume, pagination and year, and screened for retractions, corrections and expressions of concern; sources failing verification were excluded rather than corrected by inference. Analysis proceeded by mapping each developability property against its modelling status and the data conditions that explain it, and by theory construction from the resulting pattern. Three findings are reported. First, developability properties are modelled very unevenly, and the unevenness tracks the condition-specificity of available measurements rather than intrinsic scientific difficulty, making a perceived modelling frontier in substance a data-infrastructure problem. Second, because antimicrobial activity and the principal developability liabilities are driven by shared physicochemical determinants, activity-first selection followed by developability filtering concentrates candidates in the region of sequence space where those liabilities are most severe. Third, developability is route-conditional, so a single global developability score is not well defined. The paper develops a staged, four-tier prioritisation framework in which the target product profile is fixed before generation, cheap developability predictions enter the objective rather than the filter, unpredictable properties are measured early under stated conditions, and each liability is triaged as designable, formulatable or disqualifying. Six falsifiable propositions and ten developability-aware reporting criteria are derived. It is recommended that developability enter discovery models as an objective, that the field prioritise condition-annotated measurement over larger datasets, and that product profiles for African health systems carry an explicit deployability constraint.
Downloads
References
Agbo, M. C., Ezeonu, I. M., Onodagu, B. O., Ezeh, C. C., Ozioko, C. A., & Emencheta, S. C. (2024). Antimicrobial resistance markers distribution in Staphylococcus aureus from Nsukka, Nigeria. BMC Infectious Diseases, 24(1), Article 320. https://doi.org/10.1186/s12879-024-09126-1
Aguilera-Puga, M. D. C., & Plisson, F. (2024). Structure-aware machine learning strategies for antimicrobial peptide discovery. Scientific Reports, 14(1), Article 11995. https://doi.org/10.1038/s41598-024-62419-y
Aronica, P. G. A., Reid, L. M., Desai, N., Li, J., Fox, S. J., Yadahalli, S., Essex, J. W., & Verma, C. S. (2021). Computational methods and tools in antimicrobial peptide research. Journal of Chemical Information and Modeling, 61(7), 3172–3196. https://doi.org/10.1021/acs.jcim.1c00175
Badrinarayanan, S., Guntuboina, C., Mollaei, P., & Barati Farimani, A. (2024). Multi-Peptide: Multimodality leveraged language-graph learning of peptide properties. Journal of Chemical Information and Modeling, 65(1), 83– 91. https://doi.org/10.1021/acs.jcim.4c01443
Bertrand, B., Hernandez-Adame, P. L., & Munoz-Garay, C. (2025). How useful are antimicrobial peptide properties for predicting activity, selectivity, and potency?. Current Protein & Peptide Science, 26(1), 22–40. https://doi.org/10.2174/01138920373178872406 Botelho Sampaio de Oliveira, K., Lopes Leite, M., Albuquerque Cunha, V., Brito da Cunha, N., & Franco, O. L. (2023). Challenges and advances in antimicrobial peptide development. Drug Discovery Today, 28(8), Article 103629. https://doi.org/10.1016/j.drudis.2023.103629
Campoccia, D., Bottau, G., De Donno, A., Bua, G., Ravaioli, S., Capponi, E., Sotgiu, G., Bellotti, C., Costantini, S., & Arciola, C. R. (2024). Assessing cytotoxicity, proteolytic stability, and selectivity of antimicrobial peptides: Implications for orthopedic applications. International Journal of Molecular Sciences, 25(24), 13241. https://doi.org/10.3390/ijms252413241
Caselli, L., Rodrigues, G. R., Franco, O. L., & Malmsten, M. (2023). Pulmonary delivery systems for antimicrobial peptides. Critical Reviews in Biotechnology, 44(6), 963–980. https://doi.org/10.1080/07388551.2023.2254932
Chen, P., Zhang, T., Li, C., Praveen, P., Parisi, K., Beh, C., Ding, S., Wade, J. D., Hong, Y., Li, S., Nkoh, J. N., Hung, A., Li, W., & Shang, C. (2025). Aggregation-prone antimicrobial peptides target Gram-negative bacterial nucleic acids and protein synthesis. Acta Biomaterialia, 192, 446–460. https://doi.org/10.1016/j.actbio.2024.12.002
Condado-Morales, I., Dingfelder, F., Waibel, I., Turnbull, O. M., Patel, B., Cao, Z., Bjelke, J. R., Grell, S. N., Bennet, A., Hummer, A. M., Raybould, M. I. J., Deane, C. M., Egebjerg, T., Lorenzen, N., & Arosio, P. (2024). A comparative study of the developability of full-length antibodies, fragments, and bispecific formats reveals higher stability risks for engineered constructs. mAbs, 16(1), Article 2403156. https://doi.org/10.1080/19420862.2024.2403156
Decker, A. P., Su, Y., Mishra, B., Verma, A., Lushnikova, T., Xie, J., & Wang, G. (2022). Peptide stability is important but not a general requirement for antimicrobial and antibiofilm activity in vitro and in vivo. Molecular Pharmaceutics, 20(1), 738–749. https://doi.org/10.1021/acs.molpharmaceut.2c00
Ebrahimikondori, H., Sutherland, D., Yanai, A., Richter, A., Salehi, A., Li, C., Coombe, L., Kotkoff, M., Warren, R. L., & Birol, I. (2024). Structure-aware deep learning model for peptide toxicity prediction. Protein Science, 33(7), Article e5076. https://doi.org/10.1002/pro.5076
Einarson, K. A., Bendtsen, K. M., Li, K., Thomsen, M., Kristensen, N. R., Winther, O., Fulle, S., Clemmensen, L., & Refsgaard, H. H. F. (2023). Molecular representations in machine-learning-based prediction of PK parameters for insulin analogs. ACS Omega, 8(26), 23566–23578. https://doi.org/10.1021/acsomega.3c01218
Enninful, G. N., Kuppusamy, R., Tiburu, E. K., Kumar, N., & Willcox, M. D. P. (2024). Non-canonical amino acid bioincorporation into antimicrobial peptides and its challenges. Journal of Peptide Science, 30(6), Article e3560. https://doi.org/10.1002/psc.3560
Evers, A., Clénet, D., & Pfeiffer-Marek, S. (2022). Long-term stability prediction for developability assessment of biopharmaceutics using advanced kinetic modeling. Pharmaceutics, 14(2), 375. https://doi.org/10.3390/pharmaceutics14020375
Fang, Y., Ma, Y., Yu, K., Dong, J., & Zeng, W. (2024). Integrated computational approaches for advancing antimicrobial peptide development. Trends in Pharmacological Sciences, 45(11), 1046–1060. https://doi.org/10.1016/j.tips.2024.09.011
Flynn, J., Ryan, A., & Hudson, S. P. (2021). Pre-formulation and delivery strategies for the development of bacteriocins as next generation antibiotics. European Journal of Pharmaceutics and Biopharmaceutics, 165, 149–163. https://doi.org/10.1016/j.ejpb.2021.05.015
Groover, K. E., Randall, J. R., & Davies, B. W. (2024). Development of a selective and stable antimicrobial peptide. ACS Infectious Diseases, 10(6), 2151–2160. https://doi.org/10.1021/acsinfecdis.4c00142
He, S., Yang, Z., Li, X., Wu, H., Zhang, L., Shan, A., & Wang, J. (2023). Boosting stability and therapeutic potential of proteolysis-resistant antimicrobial peptides by end-tagging β-naphthylalanine. Acta Biomaterialia, 164, 175– 194. https://doi.org/10.1016/j.actbio.2023.04.030
Hermans, L. E., Centner, C. M., Morel, C. M., Mbamalu, O., Bonaconsa, C., Ferreyra, C., Lindahl, O., & Mendelson, M. (2024). Point-of-care diagnostics for infection and antimicrobial resistance in sub-Saharan Africa: A narrative review. International Journal of Infectious Diseases, 142, Article 106907. https://doi.org/10.1016/j.ijid.2023.11.027
Huang, L., Jiang, Q., Xiong, Y., Zhang, G., & Shao, D. (2025). TSLAmy: A novel amyloid hexapeptide aggregation prediction approach based on two-stage learning. IEEE Journal of Biomedical and Health Informatics, 29(10), 7741–7752. https://doi.org/10.1109/JBHI.2025.3567518
Kong, Q., Zhao, Y., Gong, H., Kang, L., Fu, J., Li, L., Wan, B., Wang, P., Li, X., Wang, Y., Zhang, J., Yu, Y., Yang, X., Zuo, X., Wang, H., & Li, Y. (2026). AI agent-based discovery of D-enantiomeric antimicrobial peptides against multidrug-resistant bacterial infection. Biomaterials, 329, Article 123927. https://doi.org/10.1016/j.biomaterials.2025.1239
Kuri, P. R., & Goswami, P. (2024). Unravelling aggregation propensity of rotavirus A VP6 expressed as E. coli inclusion bodies through in silico prediction. Scientific Reports, 14(1), Article 21464. https://doi.org/10.1038/s41598-024-69896-1
Lai, Z., Yuan, X., Chen, H., Zhu, Y., Dong, N., & Shan, A. (2022). Strategies employed in the design of antimicrobial peptides with enhanced proteolytic stability. Biotechnology Advances, 59, Article 107962. https://doi.org/10.1016/j.biotechadv.2022.10796
Li, D., Yang, Y., Li, R., Huang, L., Wang, Z., Deng, Q., & Dong, S. (2021). N-terminal acetylation of antimicrobial peptide L163 improves its stability against protease degradation. Journal of Peptide Science, 27(9), Article e3337. https://doi.org/10.1002/psc.3337
Liu, H., Song, Z., Zhang, Y., Wu, B., Chen, D., Zhou, Z., Zhang, H., Li, S., Feng, X., Huang, J., & Wang, H. (2025). De novo design of self-assembling peptides with antimicrobial activity guided by deep learning. Nature Materials, 24(8), 1295–1306. https://doi.org/10.1038/s41563-025-02164-3
Louros, N., Schymkowitz, J., & Rousseau, F. (2023). Mechanisms and pathology of protein misfolding and aggregation. Nature Reviews Molecular Cell Biology, 24(12), 912–933. https://doi.org/10.1038/s41580-023-00647-2
Lubanga, A. F., Bwanali, A. N., Kambiri, F., Harawa, G., Mudenda, S., Mpinganjira, S. L., Singano, N., Makole, T., Kapatsa, T., Kamayani, M., & Ssebibubbu, S. (2024). Tackling antimicrobial resistance in sub-Saharan Africa: Challenges and opportunities for implementing the new people-centered WHO guidelines. Expert Review of Anti-infective Therapy, 22(6), 379–386. https://doi.org/10.1080/14787210.2024.2362270
Ma, Y., Guo, Z., Xia, B., Zhang, Y., Liu, X., Yu, Y., Tang, N., Tong, X., Wang, M., Ye, X., Feng, J., Chen, Y., & Wang, J. (2022). Identification of antimicrobial peptides from the human gut microbiome using deep learning. Nature Biotechnology, 40(6), 921–931. https://doi.org/10.1038/s41587-022-01226-0
Makowski, E. K., Chen, H., Lambert, M., Bennett, E. M., Eschmann, N. S., Zhang, Y., Zupancic, J. M., Desai, A. A., Smith, M. D., Lou, W., Fernando, A., Tully, T., Gallo, C. J., Lin, L., & Tessier, P. M. (2022). Reduction of therapeutic antibody self-association using yeast-display selections and machine learning. mAbs, 14(1), Article 2146629. https://doi.org/10.1080/19420862.2022.2146629
Naghavi, M., Vollset, S. E., Ikuta, K. S., Swetschinski, L. R., Gray, A. P., Wool, E. E., Robles Aguilar, G., Mestrovic, T., Smith, G., Han, C., Hsu, R. L., Chalek, J., Araki, D. T.,
Chung, E., Raggi, C., Gershberg Hayoon, A., Davis Weaver, N., Lindstedt, P. A., Smith, A. E., . . . Murray, C. J. L. (2024). Global burden of bacterial antimicrobial resistance 1990–2021: A systematic analysis with forecasts to 2050. The Lancet, 404(10459), 1199–1226. https://doi.org/10.1016/S0140-6736(24)01867-1
Ngambenjawong, C., Chan, L. W., Fleming, H. E., & Bhatia, S. N. (2022). Conditional antimicrobial peptide therapeutics. ACS Nano, 16(10), 15779–15791. https://doi.org/10.1021/acsnano.2c04162
Oeller, M., Kang, R., Bell, R., Ausserwöger, H., Sormanni, P., & Vendruscolo, M. (2023). Sequence-based prediction of pH-dependent protein solubility using CamSol. Briefings in Bioinformatics, 24(2), Article bbad004. https://doi.org/10.1093/bib/bbad004
Onah, S. I., & Umar, H. J. (2024). Appraising Nigeria's approach to combating antimicrobial resistance. The International Journal of Health Planning and Management, 39(2), 556–562. https://doi.org/10.1002/hpm.3717
Pintado, C., Santos, J., Iglesias, V., & Ventura, S. (2021). SolupHred: A server to predict the pH-dependent aggregation of intrinsically disordered proteins. Bioinformatics, 37(11), 1602–1603. https://doi.org/10.1093/bioinformatics/btaa909
Plisson, F., Ramírez-Sánchez, O., & Martínez-Hernández, C. (2020). Machine learning-guided discovery and design of non-hemolytic peptides. Scientific Reports, 10(1), Article 16581. https://doi.org/10.1038/s41598-020-73644-6
Ragonis-Bachar, P., Axel, G., Blau, S., Ben-Tal, N., Kolodny, R., & Landau, M. (2024). What can AlphaFold do for antimicrobial amyloids?. Proteins: Structure, Function, and Bioinformatics, 92(2), 265–281. https://doi.org/10.1002/prot.26618
Ragonis-Bachar, P., Rayan, B., Barnea, E., Engelberg, Y., Upcher, A., & Landau, M. (2022). Natural antimicrobial peptides self-assemble as α/β chameleon amyloids. Biomacromolecules, 23(9), 3713–3727. https://doi.org/10.1021/acs.biomac.2c00582
Ramos-Llorens, M., Bello-Madruga, R., Valle, J., Andreu, D., & Torrent, M. (2024). PyAMPA: A high-throughput prediction and optimization tool for antimicrobial peptides. mSystems, 9(7), Article e01358-23. https://doi.org/10.1128/msystems.01358-23
Rathore, A. S., Kumar, N., Choudhury, S., Mehta, N. K., & Raghava, G. P. S. (2025). Prediction of hemolytic peptides and their hemolytic concentration. Communications Biology, 8(1), Article 176. https://doi.org/10.1038/s42003-025-07615-w
Rios, T. B., Rezende, S. B., Maximiano, M. R., Cardoso, M. H., Malmsten, M., de la Fuente-Nunez, C., & Franco, O. L. (2024). Computational approaches for antimicrobial peptide delivery. Bioconjugate Chemistry, 35(12), 1873–1882. https://doi.org/10.1021/acs.bioconjchem.4c0040
Salem, M., Keshavarzi Arshadi, A., & Yuan, J. S. (2022). AMPDeep: Hemolytic activity prediction of antimicrobial peptides using transfer learning. BMC Bioinformatics, 23(1), Article 389. https://doi.org/10.1186/s12859-022-04952-z Santos-Júnior, C. D., Pan, S., Zhao, X.-M., & Coelho, L. P. (2020). Macrel: Antimicrobial peptide screening in genomes and metagenomes. PeerJ, 8, e10555. https://doi.org/10.7717/peerj.10555 Santos-Júnior, C. D., Torres, M. D. T., Duan, Y., Rodríguez del Río, Á., Schmidt, T. S. B., Chong, H., Fullam, A., Kuhn, M., Zhu, C., Houseman, A., Somborski, J., Vines, A., Zhao, X.-M., Bork, P., Huerta-Cepas, J., de la Fuente-Nunez, C., & Coelho, L. P. (2024). Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell, 187(14), 3761–3778.e16. https://doi.org/10.1016/j.cell.2024.05.013
Sidorczuk, K., Gagat, P., Pietluch, F., Kała, J., Rafacz, D., Bąkała, L., Słowik, J., Kolenda, R., Rödiger, S., Fingerhut, L. C. H. W., Cooke, I. R., Mackiewicz, P., & Burdukiewicz, M. (2022). Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data. Briefings in Bioinformatics, 23(5), Article bbac343. https://doi.org/10.1093/bib/bbac343
Siegmund, V., Klewinghaus, D., Teroerde, J., Pekar, L., Harwardt, J., Zielonka, S., Militano, F., Licari, G., & Evers, A. (2025). Optimizing colloidal stability and viscosity of multispecific antibodies at the drug discovery-development interface: A systematic predictive case study. mAbs, 17(1), Article 2553622. https://doi.org/10.1080/19420862.2025.2553622
Sormanni, P., & Vendruscolo, M. (2019). Protein solubility predictions using the CamSol method in the study of protein homeostasis. Cold Spring Harbor Perspectives in Biology, 11(12), Article a033845. https://doi.org/10.1101/cshperspect.a033845
Szymczak, P., & Szczurek, E. (2023). Artificial intelligence-driven antimicrobial peptide discovery. Current Opinion in Structural Biology, 83, Article 102733. https://doi.org/10.1016/j.sbi.2023.102733
Tang, Y., Zhang, Y., Zhang, D., Liu, Y., Nussinov, R., & Zheng, J. (2024). Exploring pathological link between antimicrobial and amyloid peptides. Chemical Society Reviews, 53(17), 8713–8763. https://doi.org/10.1039/d3cs00878a
Totaro, V., Guido, G., Cotugno, S., De Vita, E., Asaduzzaman, M., Patti, G., Segala, F. V., Putoto, G., Frallonardo, L., Farkas, F. B., Lakatos, B., Veronese, N., Locantore, P., Di Gennaro, F., & Saracino, A. (2025). Antimicrobial resistance in sub-Saharan Africa: A comprehensive landscape review. The American Journal of Tropical Medicine and Hygiene, 113(2), 253–263. https://doi.org/10.4269/ajtmh.25-0035
Tran, D. V. H., Luong, H. X., Kim, D.-H., Lee, B.-J., & Kim, Y.-W. (2024). Lysine-homologue substitution: Impact on antimicrobial activity and proteolytic stability of cationic stapled heptapeptides. Bioorganic & Medicinal Chemistry, 106, Article 117735. https://doi.org/10.1016/j.bmc.2024.117735
Vincenzi, M., Mercurio, F. A., & Leone, M. (2024). Virtual screening of peptide libraries: The search for peptide-based therapeutics using computational tools. International Journal of Molecular Sciences, 25(3), 1798. https://doi.org/10.3390/ijms25031798
Wan, F., Torres, M. D. T., Peng, J., & de la Fuente-Nunez, C. (2024). Deep-learning-enabled antibiotic discovery through molecular de-extinction. Nature Biomedical Engineering, 8(7), 854–871. https://doi.org/10.1038/s41551-024-01201-x
Wang, B., Lin, P., Zhong, Y., Tan, X., Shen, Y., Huang, Y., Jin, K., Zhang, Y., Zhan, Y., Shen, D., Wang, M., Yu, Z., & Wu, Y. (2025). Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens. Nature Microbiology, 10(2), 332–347. https://doi.org/10.1038/s41564-024-01907-3
Wang, C., Hong, T., Cui, P., Wang, J., & Xia, J. (2021). Antimicrobial peptides towards clinical application: Delivery and formulation. Advanced Drug Delivery Reviews, 175, Article 113818. https://doi.org/10.1016/j.addr.2021.05.028
Wang, C., Garlick, S., & Zloh, M. (2021). Deep learning for novel antimicrobial peptide design. Biomolecules, 11(3), 471. https://doi.org/10.3390/biom11030471
Wang, G. (2023). The antimicrobial peptide database is 20 years old: Recent developments and future directions. Protein Science, 32(10), Article e4778. https://doi.org/10.1002/pro.4778
Wang, G., Schmidt, C., Li, X., & Wang, Z. (2025). APD6: The antimicrobial peptide database is expanded to promote research and development by deploying an unprecedented information pipeline. Nucleic Acids Research, 54(D1), D363–D374. https://doi.org/10.1093/nar/gkaf860
Wang, J., Feng, J., Kang, Y., Pan, P., Ge, J., Wang, Y., Wang, M., Wu, Z., Zhang, X., Yu, J., Zhang, X., Wang, T., Wen, L., Yan, G., Deng, Y., Shi, H., Hsieh, C.-Y., Jiang, Z., & Hou, T. (2025). Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model. Science Advances, 11(10), Article eads8932. https://doi.org/10.1126/sciadv.ads8932
Wang, Y., Song, M., Liu, F., Liang, Z., Hong, R., Dong, Y., Luan, H., Fu, X., Yuan, W., Fang, W., Li, G., Lou, H., & Chang, W. (2025). Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides. Science Advances, 11(6), Article eadp7171. https://doi.org/10.1126/sciadv.adp7171
Wang, Y., Zhao, L., Li, Z., Xi, Y., Pan, Y., Zhao, G., & Zhang, L. (2025). A generative artificial intelligence approach for the discovery of antimicrobial peptides against multidrug-resistant bacteria. Nature Microbiology, 10(11), 2997–3012. https://doi.org/10.1038/s41564-025-02114-4
Wang, Y., Chang, R. Y. K., Britton, W. J., & Chan, H.-K. (2022). Advances in the development of antimicrobial peptides and proteins for inhaled therapy. Advanced Drug Delivery Reviews, 180, Article 114066. https://doi.org/10.1016/j.addr.2021.114066
Xue, J., Fu, Y., Li, H., Zhang, T., Cong, W., Hu, H., Lu, Z., Yan, F., & Li, Y. (2024). All- hydrocarbon stapling enables improvement of antimicrobial activity and proteolytic stability of peptide Figainin 2. Journal of Peptide Science, 30(6), Article e3566. https://doi.org/10.1002/psc.3566
Yang, S., & Duncan, G. A. (2023). Synthetic mucus biomaterials for antimicrobial peptide delivery. Journal of Biomedical Materials Research Part A, 111(10), 1616–1626. https://doi.org/10.1002/jbm.a.37559
Yao, L., Liu, Q., Lei, Z., & Sun, T. (2023). Development and challenges of antimicrobial peptide delivery strategies in bacterial therapy: A review. International Journal of Biological Macromolecules, 253, Article 126819. https://doi.org/10.1016/j.ijbiomac.2023.126819
Yu, H., Wang, R., Qiao, J., & Wei, L. (2023). Multi-CGAN: Deep generative model-based multiproperty antimicrobial peptide design. Journal of Chemical Information and Modeling, 64(1), 316–326. https://doi.org/10.1021/acs.jcim.3c01881
Zakharova, E., Orsi, M., Capecchi, A., & Reymond, J.-L. (2022). Machine learning guided discovery of non-hemolytic membrane disruptive anticancer peptides. ChemMedChem, 17(17), Article e202200291. https://doi.org/10.1002/cmdc.202200291
Zhao, H., Sun, J., Cheng, Y., Nie, S., & Li, W. (2025). Advances in peptide/polymer antimicrobial assemblies. Journal of Materials Chemistry B, 13(5), 1518–1530. https://doi.org/10.1039/d4tb02144d
Zheng, S., Tu, Y., Li, B., Qu, G., Li, A., Peng, X., Li, S., & Shao, C. (2025). Antimicrobial peptide biological activity, delivery systems and clinical translation status and challenges. Journal of Translational Medicine, 23(1), Article 292. https://doi.org/10.1186/s12967-025-06321-9
Downloads
Published
Data Availability Statement
Research data supporting this study was provided with the submission and is held by the editorial office.Issue
Section
License
Copyright (c) 2026 Igwe Emmanuel Sunday, AGBO Chinonso Anthony, NWOGBOJI Emmanuel Usulor (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with the Journal of Student
Research (JSR) retain copyright of their work
and grant JSR the right of first publication
under a Creative Commons Attribution 4.0
International License (CC BY 4.0).
This license permits others to share, copy,
redistribute, and adapt the work in any medium
or format for any purpose, provided appropriate
credit is given to the original authors and
source, a link to the license is provided, and
any changes made are indicated.
Full article access requires payment of NGN
2,000 (download) or NGN 1,000 (view only).
License URL: https://creativecommons.org/licenses/by/4.0/
