Exploration of Drug Science

Table 2. Specialized antimicrobial peptide (AMP) databases.

Name	Sequence	Description	Update year of the current version	Remarks	Reference
PlantPepDB	3,848	AMPs derived from plants	2020	Activity, hemolytic activity, cytotoxicity	[65]
ACovPepDB	518	Anti-coronavirus peptides	2022	Organism, activity, mechanism	[66]
AVPdb	2,683	Antiviral peptide	2014	Organism, activity, mechanism	[67]
HIPdb	981	HIV inhibiting peptides	2013	Organism, activity, mechanism	[68]
DRAVP	5,688	Antiviral peptide	2023	Antiviral activity, structure information, physicochemical information, and literature information	[69]
DCTPep	6,214	Cancer therapy peptides	2024	Tumor active peptide, cancer targeted peptides, targeted peptide conjugates, and collection of targeted peptides	[70]

Return to article view

Declarations

Author contributions

YZ: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. ZW: Investigation, Writing—original draft, Writing—review & editing. HZ: Validation, Writing—review & editing, Supervision, Funding acquisition. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

National Natural Science Foundation of China [82073767]; Priority Academic Program Development of Jiangsu Higher Education Institutions [PAPD 2023007]; 2026 College Students’ Innovation and Entrepreneurship Training Program [2026303]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

Publisher’s note

Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.

References

2021 Antibacterial agents in clinical and preclinical development: an overview and analysis [Internet]. WHO; c2026 [cited 2025 July 21]. Available from: https://www.who.int/publications/i/item/9789240047655

Cook MA, Wright GD. The past, present, and future of antibiotics. Sci Transl Med. 2022;14:eabo7793. [DOI] [PubMed]

Sinha S, Upadhyay LSB. Understanding antimicrobial resistance (AMR) mechanisms and advancements in AMR diagnostics. Diagn Microbiol Infect Dis. 2025;113:116949. [DOI] [PubMed]

Moretta A, Scieuzo C, Petrone AM, Salvia R, Manniello MD, Franco A, et al. Antimicrobial Peptides: A New Hope in Biomedical and Pharmaceutical Fields. Front Cell Infect Microbiol. 2021;11:668632. [DOI] [PubMed] [PMC]

Brizuela CA, Liu G, Stokes JM, de la Fuente-Nunez C. AI Methods for Antimicrobial Peptides: Progress and Challenges. Microb Biotechnol. 2025;18:e70072. [DOI] [PubMed] [PMC]

Huan Y, Kong Q, Mou H, Yi H. Antimicrobial Peptides: Classification, Design, Application and Research Progress in Multiple Fields. Front Microbiol. 2020;11:582779. [DOI] [PubMed] [PMC]

Torres MT, de la Fuente-Nunez C. Toward computer-made artificial antibiotics. Curr Opin Microbiol. 2019;51:30–8. [DOI] [PubMed]

Pirtskhalava M, Amstrong AA, Grigolava M, Chubinidze M, Alimbarashvili E, Vishnepolsky B, et al. DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Res. 2021;49:D288–97. [DOI] [PubMed] [PMC]

Zhang K, Yang X, Wang Y, Yu Y, Huang N, Li G, et al. Artificial intelligence in drug development. Nat Med. 2025;31:45–59. [DOI] [PubMed]

10.

Jiang Z, Feng J, Wang F, Wang J, Wang N, Zhang M, et al. AI-Guided Design of Antimicrobial Peptide Hydrogels for Precise Treatment of Drug-resistant Bacterial Infections. Adv Mater. 2025;37:2500043. [DOI] [PubMed]

11.

Mishra B, Basu A, Shehadeh F, Felix L, Kollala SS, Chhonker YS, et al. Antimicrobial peptide developed with machine learning sequence optimization targets drug resistant Staphylococcus aureus in mice. J Clin Invest. 2025;135:e185430. [DOI] [PubMed] [PMC]

12.

Darby EM, Trampari E, Siasat P, Gaya MS, Alav I, Webber MA, et al. Molecular mechanisms of antibiotic resistance revisited. Nat Rev Microbiol. 2023;21:280–95. [DOI] [PubMed]

13.

Catalano A, Iacopetta D, Ceramella J, Scumaci D, Giuzio F, Saturnino C, et al. Multidrug Resistance (MDR): A Widespread Phenomenon in Pharmacological Therapies. Molecules. 2022;27:616. [DOI] [PubMed] [PMC]

14.

Kumar P, Kizhakkedathu JN, Straus SK. Antimicrobial Peptides: Diversity, Mechanism of Action and Strategies to Improve the Activity and Biocompatibility In Vivo. Biomolecules. 2018;8:4. [DOI] [PubMed] [PMC]

15.

Yang J, Zhang J, Feng Z, Ma Y. The Role and Mechanisms of Antimicrobial Peptides in Overcoming Multidrug-Resistant Bacteria. Molecules. 2025;30:128. [DOI] [PubMed] [PMC]

16.

K R G, Balenahalli Narasingappa R, Vishnu Vyas G. Unveiling mechanisms of antimicrobial peptide: Actions beyond the membranes disruption. Heliyon. 2024;10:e38079. [DOI] [PubMed] [PMC]

17.

Marquette A, Bechinger B. Biophysical Investigations Elucidating the Mechanisms of Action of Antimicrobial Peptides and Their Synergism. Biomolecules. 2018;8:18. [DOI] [PubMed] [PMC]

18.

Malanovic N, Lohner K. Antimicrobial Peptides Targeting Gram-Positive Bacteria. Pharmaceuticals (Basel). 2016;9:59. [DOI] [PubMed] [PMC]

19.

Panina I, Krylov N, Nolde D, Efremov R, Chugunov A. Environmental and dynamic effects explain how nisin captures membrane-bound lipid II. Sci Rep. 2020;10:8821. [DOI] [PubMed] [PMC]

20.

Oren Z, Shai Y. Mode of action of linear amphipathic alpha-helical antimicrobial peptides. Biopolymers. 1998;47:451–63. [DOI] [PubMed]

21.

Matsuzaki K, Sugishita K, Ishibe N, Ueha M, Nakata S, Miyajima K, et al. Relationship of membrane curvature to the formation of pores by magainin 2. Biochemistry. 1998;37:11856–63. [DOI] [PubMed]

22.

Yoneyama F, Imura Y, Ohno K, Zendo T, Nakayama J, Matsuzaki K, et al. Peptide-lipid huge toroidal pore, a new antimicrobial mechanism mediated by a lactococcal bacteriocin, lacticin Q. Antimicrob Agents Chemother. 2009;53:3211–7. [DOI] [PubMed] [PMC]

23.

Yasir M, Willcox MDP, Dutta D. Action of Antimicrobial Peptides against Bacterial Biofilms. Materials (Basel). 2018;11:2468. [DOI] [PubMed] [PMC]

24.

Martinez M, Gonçalves S, Felício MR, Maturana P, Santos NC, Semorile L, et al. Synergistic and antibiofilm activity of the antimicrobial peptide P5 against carbapenem-resistant Pseudomonas aeruginosa. Biochim Biophys Acta Biomembr. 2019;1861:1329–37. [DOI] [PubMed]

25.

Nicolas P. Multifunctional host defense peptides: intracellular-targeting antimicrobial peptides. FEBS J. 2009;276:6483–96. [DOI] [PubMed]

26.

Ruffolo JA, Madani A. Designing proteins with language models. Nat Biotechnol. 2024;42:200–2. [DOI] [PubMed]

27.

Benfield AH, Henriques ST. Mode-of-Action of Antimicrobial Peptides: Membrane Disruption vs. Intracellular Mechanisms. Front Med Technol. 2020;2:610997. [DOI] [PubMed] [PMC]

28.

Yang H, Fu J, Zhao Y, Shi H, Hu H, Wang H. Escherichia coli PagP Enzyme-Based De Novo Design and In Vitro Activity of Antibacterial Peptide LL-37. Med Sci Monit. 2017;23:2558–64. [DOI] [PubMed] [PMC]

29.

Bhattacharjya S, Zhang Z, Ramamoorthy A. LL-37: Structures, Antimicrobial Activity, and Influence on Amyloid-Related Diseases. Biomolecules. 2024;14:320. [DOI] [PubMed] [PMC]

30.

Schneider T, Kruse T, Wimmer R, Wiedemann I, Sass V, Pag U, et al. Plectasin, a fungal defensin, targets the bacterial cell wall precursor Lipid II. Science. 2010;328:1168–72. [DOI] [PubMed]

31.

Casteels P, Ampe C, Jacobs F, Vaeck M, Tempst P. Apidaecins: antibacterial peptides from honeybees. EMBO J. 1989;8:2387–91. [DOI] [PubMed] [PMC]

32.

Skowron KJ, Baliga C, Johnson T, Kremiller KM, Castroverde A, Dean TT, et al. Structure-Activity Relationships of the Antimicrobial Peptide Natural Product Apidaecin. J Med Chem. 2023;66(17):11831–42. [DOI] [PubMed] [PMC]

33.

Xuan J, Feng W, Wang J, Wang R, Zhang B, Bo L, et al. Antimicrobial peptides for combating drug-resistant bacterial infections. Drug Resist Updat. 2023;68:100954. [DOI] [PubMed]

34.

Liu C, Qi J, Shan B, Gao R, Gao F, Xie H, et al. Pretreatment with cathelicidin-BF ameliorates Pseudomonas aeruginosa pneumonia in mice by enhancing NETosis and the autophagy of recruited neutrophils and macrophages. Int Immunopharmacol. 2018;65:382–91. [DOI] [PubMed]

35.

Marks LR, Clementi EA, Hakansson AP. The human milk protein-lipid complex HAMLET sensitizes bacterial pathogens to traditional antimicrobial agents. PLoS One. 2012;7:e43514. [DOI] [PubMed] [PMC]

36.

Matsumoto K, Yamazaki K, Kawakami S, Miyoshi D, Ooi T, Hashimoto S, et al. In vivo target exploration of apidaecin based on Acquired Resistance induced by Gene Overexpression (ARGO assay). Sci Rep. 2017;7:12136. [DOI] [PubMed] [PMC]

37.

Ghosh A, Kar RK, Jana J, Saha A, Jana B, Krishnamoorthy J, et al. Indolicidin targets duplex DNA: structural and mechanistic insight through a combination of spectroscopy and microscopy. ChemMedChem. 2014;9:2052–8. [DOI] [PubMed]

38.

Lai PK, Kaznessis YN. Insights into Membrane Translocation of Protegrin Antimicrobial Peptides by Multistep Molecular Dynamics Simulations. ACS Omega. 2018;3:6056–65. [DOI] [PubMed] [PMC]

39.

Tian C, Hao Y, Fu H, Shao X, Cai W. From AI-Driven Sequence Generation to Molecular Simulation: A Comprehensive Framework for Antimicrobial Peptide Discovery. J Chem Inf Model. 2025;65:9566–75. [DOI] [PubMed]

40.

Li Q, Xu Z, Zhu Y, Zhou W, Guan M, Zhao S, et al. DTBind: A Mechanism-Driven Deep Learning Framework for Accurate Prediction of Drug-Target Molecular Recognition. Research (Wash D C). 2025;8:1022. [DOI] [PubMed] [PMC]

41.

Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46:W296–303. [DOI] [PubMed] [PMC]

42.

Ramazi S, Mohammadi N, Allahverdi A, Khalili E, Abdolmaleki P. A review on antimicrobial peptides databases and the computational tools. Database (Oxford). 2022;2022:baac011. [DOI] [PubMed] [PMC]

43.

Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024;630:493–500. [DOI] [PubMed] [PMC]

44.

Chen L, Li Q, Nasif KFA, Xie Y, Deng B, Niu S, et al. AI-Driven Deep Learning Techniques in Protein Structure Prediction. Int J Mol Sci. 2024;25:8426. [DOI] [PubMed] [PMC]

45.

Tian C, Liu X, Hao Y, Fu H, Shao X, Cai W. Flexible Tail of Antimicrobial Peptide PGLa Facilitates Water Pore Formation in Membranes. J Phys Chem B. 2025;129:1453–61. [DOI] [PubMed]

46.

Jiménez-Osés G, Peccati F. Structure Prediction of Alternate Frame Folding Systems with AlphaFold3. J Chem Inf Model. 2025;65:8229–37. [DOI] [PubMed] [PMC]

47.

Musin K, Asyanova E. How Machine Learning Helps in Combating Antimicrobial Resistance: A Review of AMP Analysis and Generation Methods. Int J Pept Res Ther. 2025;31:59. [DOI]

48.

Nawaz M, Huiyuan Y, Akhtar F, Tianyue M, Zheng H. Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools. Mol Divers. 2025;29:3753–88. [DOI] [PubMed]

49.

Mondal RK, Sen D, Arya A, Samanta SK. Developing anti-microbial peptide database version 1 to provide comprehensive and exhaustive resource of manually curated AMPs. Sci Rep. 2023;13:17843. [DOI] [PubMed] [PMC]

50.

Yao L, Guan J, Xie P, Chung CR, Zhao Z, Dong D, et al. dbAMP 3.0: updated resource of antimicrobial activity and structural annotation of peptides in the post-pandemic era. Nucleic Acids Res. 2025;53:D364–76. [DOI] [PubMed] [PMC]

51.

Jhong JH, Chi YH, Li WC, Lin TH, Huang KY, Lee TY. dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data. Nucleic Acids Res. 2019;47:D285–97. [DOI] [PubMed] [PMC]

52.

Ma T, Liu Y, Yu B, Sun X, Yao H, Hao C, et al. DRAMP 4.0: an open-access data repository dedicated to the clinical translation of antimicrobial peptides. Nucleic Acids Res. 2025;53:D403–10. [DOI] [PubMed] [PMC]

53.

Fan L, Sun J, Zhou M, Zhou J, Lao X, Zheng H, et al. DRAMP: a comprehensive data repository of antimicrobial peptides. Sci Rep. 2016;6:24482. [DOI] [PubMed] [PMC]

54.

Kang X, Dong F, Shi C, Liu S, Sun J, Chen J, et al. DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci Data. 2019;6:148. [DOI] [PubMed] [PMC]

55.

Shi G, Kang X, Dong F, Liu Y, Zhu N, Hu Y, et al. DRAMP 3.0: an enhanced comprehensive data repository of antimicrobial peptides. Nucleic Acids Res. 2022;50:D488–96. [DOI] [PubMed] [PMC]

56.

Pirtskhalava M, Gabrielian A, Cruz P, Griggs HL, Squires RB, Hurt DE, et al. DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides. Nucleic Acids Res. 2016;44:D1104–12. [DOI] [PubMed] [PMC]

57.

Ye G, Wu H, Huang J, Wang W, Ge K, Li G, et al. LAMP2: a major update of the database linking antimicrobial peptides. Database (Oxford). 2020;2020:baaa061. [DOI] [PubMed] [PMC]

58.

Waghu FH, Barai RS, Gurung P, Idicula-Thomas S. CAMP_R3: a database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res. 2016;44:D1094–7. [DOI] [PubMed] [PMC]

59.

Gawde U, Chakraborty S, Waghu FH, Barai RS, Khanderkar A, Indraguru R, et al. CAMP_R4: a database of natural and synthetic antimicrobial peptides. Nucleic Acids Res. 2023;51:D377–83. [DOI] [PubMed] [PMC]

60.

Wang G, Li X, Wang Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 2016;44:D1087–93. [DOI] [PubMed] [PMC]

61.

Wang Z, Wang G. APD: the Antimicrobial Peptide Database. Nucleic Acids Res. 2004;32:D590–2. [DOI] [PubMed] [PMC]

62.

Wang G, Schmidt C, Li X, Wang Z. APD6: the antimicrobial peptide database is expanded to promote research and development by deploying an unprecedented information pipeline. Nucleic Acids Res. 2026;54:D363–74. [DOI] [PubMed] [PMC]

63.

Piotto SP, Sessa L, Concilio S, Iannelli P. YADAMP: yet another database of antimicrobial peptides. Int J Antimicrob Agents. 2012;39:346–51. [DOI] [PubMed]

64.

Gómez EA, Giraldo P, Orduz S. InverPep: A database of invertebrate antimicrobial peptides. J Glob Antimicrob Resist. 2017;8:13–7. [DOI] [PubMed]

65.

Das D, Jaiswal M, Khan FN, Ahamad S, Kumar S. PlantPepDB: A manually curated plant peptide database. Sci Rep. 2020;10:2194. [DOI] [PubMed] [PMC]

66.

Zhang Q, Chen X, Li B, Lu C, Yang S, Long J, et al. A database of anti-coronavirus peptides. Sci Data. 2022;9:294. [DOI] [PubMed] [PMC]

67.

Qureshi A, Thakur N, Tandon H, Kumar M. AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses. Nucleic Acids Res. 2014;42:D1147–53. [DOI] [PubMed] [PMC]

68.

Qureshi A, Thakur N, Kumar M. HIPdb: a database of experimentally validated HIV inhibiting peptides. PLoS One. 2013;8:e54908. [DOI] [PubMed] [PMC]

69.

Nawaz M, Yao H, Liu H, Akhtar F, Ma T, Chen Y, et al. DRAVP 2.0: A Curated and Genomically Annotated Database of Antiviral Peptides and Proteins. J Mol Biol. 2026; 5:169628. [DOI] [PubMed]

70.

Sun X, Liu Y, Ma T, Zhu N, Lao X, Zheng H. DCTPep, the data of cancer therapy peptides. Sci Data. 2024;11:541. [DOI] [PubMed] [PMC]

71.

Wang G. The antimicrobial peptide database is 20 years old: Recent developments and future directions. Protein Sci. 2023;32:e4778. [DOI] [PubMed] [PMC]

72.

He R, Di Bonaventura I, Visini R, Gan BH, Fu Y, Probst D, et al. Design, crystal structure and atomic force microscopy study of thioether ligated D,L-cyclic antimicrobial peptides against multidrug resistant Pseudomonas aeruginosa. Chem Sci. 2017;8:7464–75. [DOI] [PubMed] [PMC]

73.

Kumar N, Bhagwat P, Singh S, Pillai S. A review on the diversity of antimicrobial peptides and genome mining strategies for their prediction. Biochimie. 2024;227:99–115. [DOI] [PubMed]

74.

Kaweewan I, Hemmi H, Komaki H, Harada S, Kodani S. Isolation and structure determination of a new lasso peptide specialicin based on genome mining. Bioorg Med Chem. 2018;26:6050–5. [DOI] [PubMed]

75.

Li W, Huang B, Guo M, Zeng Z, Cai T, Feng L, et al. Unveiling the evolution of antimicrobial peptides in gut microbes via foundation-model-powered framework. Cell Rep. 2025;44:115773. [DOI] [PubMed]

76.

Lata S, Sharma BK, Raghava GP. Analysis and prediction of antibacterial peptides. BMC Bioinformatics. 2007;8:263. [DOI] [PubMed] [PMC]

77.

Akhter S, Miller JH. BPAGS: a web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier. Front Bioinform. 2024;3:1284705. [DOI] [PubMed] [PMC]

78.

Lira F, Perez PS, Baranauskas JA, Nozawa SR. Prediction of antimicrobial activity of synthetic peptides by a decision tree model. Appl Environ Microbiol. 2013;79:3156–9. [DOI] [PubMed] [PMC]

79.

Elalouf A, Elalouf H, Rosenfeld A, Maoz H. Artificial intelligence in drug resistance management. 3 Biotech. 2025;15:126. [DOI] [PubMed] [PMC]

80.

Xiao X, Wang P, Lin WZ, Jia JH, Chou KC. iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem. 2013;436:168–77. [DOI] [PubMed]

81.

Bai LY, Dai H, Xu Q, Junaid M, Peng SL, Zhu X, et al. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm. Int J Mol Sci. 2018;19:467. [DOI] [PubMed] [PMC]

82.

Jana T, Sarkar D, Ganguli D, Mukherjee SK, Mandal RS, Das S. ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques. Indian J Med Res. 2024;159:78–90. [DOI] [PubMed] [PMC]

83.

Wang CC, Hung YT, Chou CY, Hsuan SL, Chen ZW, Chang PY, et al. Using random forest to predict antimicrobial minimum inhibitory concentrations of nontyphoidal Salmonella in Taiwan. Vet Res. 2023;54:11. [DOI] [PubMed] [PMC]

84.

Han J, Zhang S, Liu J. Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network. STAR Protoc. 2023;4:102541. [DOI] [PubMed] [PMC]

85.

Bournez C, Riool M, de Boer L, Cordfunke RA, de Best L, van Leeuwen R, et al. CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides. Antibiotics (Basel). 2023;12:725. [DOI] [PubMed] [PMC]

86.

Lv H, Yan K, Guo Y, Zou Q, Hesham AE, Liu B. AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning. Comput Biol Med. 2022;146:105577. [DOI] [PubMed]

87.

Jaiswal M, Singh A, Kumar S. PTPAMP: prediction tool for plant-derived antimicrobial peptides. Amino Acids. 2023;55:1–17. [DOI] [PubMed]

88.

Lin C, Xiong S, Cui F, Zhang Z, Shi H, Wei L. Deep Learning in Antimicrobial Peptide Prediction. J Chem Inf Model. 2025;65:7373–92. [DOI] [PubMed]

89.

Ma Y, Guo Z, Xia B, Zhang Y, Liu X, Yu Y, et al. Identification of antimicrobial peptides from the human gut microbiome using deep learning. Nat Biotechnol. 2022;40:921–31. [DOI] [PubMed]

90.

Yan J, Cai J, Zhang B, Wang Y, Wong DF, Siu SWI. Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning. Antibiotics (Basel). 2022;11:1451. [DOI] [PubMed] [PMC]

91.

Xing W, Zhang J, Li C, Huo Y, Dong G. iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model. Brief Bioinform. 2024;25:bbad443. [DOI] [PubMed] [PMC]

92.

Wang R, Wang T, Zhuo L, Wei J, Fu X, Zou Q, et al. Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization. Brief Bioinform. 2024;25:bbae078. [DOI] [PubMed] [PMC]

93.

Lee H, Lee S, Lee I, Nam H. AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model. Protein Sci. 2023;32:e4529. [DOI] [PubMed] [PMC]

94.

Lobo F, González MS, Boto A, Pérez de la Lastra JM. Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models. Int J Mol Sci. 2023;24:10270. [DOI] [PubMed] [PMC]

95.

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention Is All You Need. arXiv:1706.03762 [Preprint]. 2017 [cited 2025 August 14]. Available from: https://doi.org/10.48550/arXiv.1706.03762

96.

Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023;379:1123–30. [DOI] [PubMed]

97.

Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Wang Y, Jones L, et al. ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. IEEE Trans Pattern Anal Mach Intell. 2022;44:7112–27. [DOI] [PubMed]

98.

Chen LT, Quinn Z, Dumas M, Peng C, Hong L, Lopez-Gonzalez M, et al. Target sequence-conditioned design of peptide binders using masked language modeling. Nat Biotechnol. 2025;[Epub ahead of print]. [DOI] [PubMed]

99.

Cao Q, Ge C, Wang X, Harvey PJ, Zhang Z, Ma Y, et al. Designing antimicrobial peptides using deep learning and molecular dynamic simulations. Brief Bioinform. 2023;24:bbad058. [DOI] [PubMed]

100.

Al-Omari AM, Akkam YH, Zyout A, Younis S, Tawalbeh SM, Al-Sawalmeh K, et al. Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery. PLoS One. 2024;19:e0315477. [DOI] [PubMed] [PMC]

101.

Yan K, Lv H, Guo Y, Peng W, Liu B. sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure. Bioinformatics. 2023;39:btac715. [DOI] [PubMed] [PMC]

102.

Sagendorf JM, Mitra R, Huang J, Chen XS, Rohs R. Structure-based prediction of protein-nucleic acid binding using graph neural networks. Biophys Rev. 2024;16:297–314. [DOI] [PubMed] [PMC]

103.

Wan F, Torres MDT, Peng J, de la Fuente-Nunez C. Deep-learning-enabled antibiotic discovery through molecular de-extinction. Nat Biomed Eng. 2024;8:854–71. [DOI] [PubMed] [PMC]

104.

Wang X, Wu Z, Wang R, Gao X. UniproLcad: Accurate Identification of Antimicrobial Peptide by Fusing Multiple Pre-Trained Protein Language Models. Symmetry. 2024;16:464. [DOI]

105.

Rives A, Meier J, Sercu T, Goyal S, Lin Z, Liu J, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci U S A. 2021;118:e2016239118. [DOI] [PubMed] [PMC]

106.

Yu Q, Dong Z, Fan X, Zong L, Li Y. HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides. arXiv:2111.06023 [Preprint]. 2021 [cited 2025 August 18]. Available from: https://doi.org/10.48550/arXiv.2111.06023

107.

Guntuboina C, Das A, Mollaei P, Kim S, Barati Farimani A. PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction. J Phys Chem Lett. 2023;14:10427–34. [DOI] [PubMed] [PMC]

108.

Lin C, Xiong S, Li J, Cui F, Zhang Z, Shi H, et al. PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction. J Cheminform. 2026;18:15. [DOI] [PubMed] [PMC]

109.

Li T, Ren X, Luo X, Wang Z, Li Z, Luo X, et al. A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection. Nat Commun. 2024;15:7538. [DOI] [PubMed] [PMC]

110.

Li Y, Xu X, Zhang X, Xu Z, Zhao J, Zhu R, et al. BroadAMP-GPT: AI-Driven generation of broad-spectrum antimicrobial peptides for combating multidrug-resistant ESKAPE pathogens. Gut Microbes. 2025;17:2523811. [DOI] [PubMed] [PMC]

111.

Yang Y, Shi R, Li Z, Jiang S, Lu BL, Zhao Q, et al. BatGPT-Chem: A Foundation Large Model for Chemical Engineering. Research (Wash D C). 2025;8:0827. [DOI] [PubMed] [PMC]

112.

Szymczak P, Możejko M, Grzegorzek T, Jurczak R, Bauer M, Neubauer D, et al. Discovering highly potent antimicrobial peptides with deep generative model HydrAMP. Nat Commun. 2023;14:1453. [DOI] [PubMed] [PMC]

113.

Yu H, Wang R, Qiao J, Wei L. Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide Design. J Chem Inf Model. 2024;64:316–26. [DOI] [PubMed]

114.

Szymczak P, Zarzecki W, Wang J, Duan Y, Wang J, Coelho LP, et al. AI-Driven Antimicrobial Peptide Discovery: Mining and Generation. Acc Chem Res. 2025;58:1831–46. [DOI] [PubMed] [PMC]

115.

Chen X, Li C, Bernards MT, Shi Y, Shao Q, He Y. Sequence-based peptide identification, generation, and property prediction with deep learning: a review. Mol Syst Des Eng. 2021;6:406–28. [DOI]

116.

Chung CR, Jhong JH, Wang Z, Chen S, Wan Y, Horng JT, et al. Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms. Int J Mol Sci. 2020;21:986. [DOI] [PubMed] [PMC]

117.

Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2. [DOI] [PubMed] [PMC]

118.

Kondratenko Y, Korobeynikov A, Lapidus A. CDSnake: Snakemake pipeline for retrieval of annotated OTUs from paired-end reads using CD-HIT utilities. BMC Bioinformatics. 2020;21:303. [DOI] [PubMed] [PMC]

119.

Lertampaiporn S, Vorapreeda T, Hongsthong A, Thammarongtham C. Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs. Genes (Basel). 2021;12:137. [DOI] [PubMed] [PMC]

120.

Amerifar S, Norouzi M, Ghandi M. A tool for feature extraction from biological sequences. Brief Bioinform. 2022;23:bbac108. [DOI] [PubMed]

121.

Das P, Sercu T, Wadhawan K, Padhi I, Gehrmann S, Cipcigan F, et al. Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations. Nat Biomed Eng. 2021;5:613–23. [DOI] [PubMed]

122.

Gligorijević V, Renfrew PD, Kosciolek T, Leman JK, Berenberg D, Vatanen T, et al. Structure-based protein function prediction using graph convolutional networks. Nat Commun. 2021;12:3168. [DOI] [PubMed] [PMC]

123.

Li W, Zhu E, Wang S, Guo X. Graph Clustering with High-Order Contrastive Learning. Entropy (Basel). 2023;25:1432. [DOI] [PubMed] [PMC]

124.

Yang K, Liu Y, Zhao Z, Ding P, Zhao W. Local structure-aware graph contrastive representation learning. Neural Netw. 2024;172:106083. [DOI] [PubMed]

125.

Jiao P, Chen H, Tang H, Bao Q, Zhang L, Zhao Z, et al. Contrastive representation learning on dynamic networks. Neural Netw. 2024;174:106240. [DOI] [PubMed]

126.

Ramos-Llorens M, Bello-Madruga R, Valle J, Andreu D, Torrent M. PyAMPA: a high-throughput prediction and optimization tool for antimicrobial peptides. mSystems. 2024;9:e01358-23. [DOI] [PubMed] [PMC]

127.

Li C, Warren RL, Birol I. Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction. BMC Res Notes. 2023;16:11. [DOI] [PubMed] [PMC]

128.

Zhou W, Liu Y, Li Y, Kong S, Wang W, Ding B, et al. TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides. Patterns (N Y). 2023;4:100702. [DOI] [PubMed] [PMC]

129.

Yan J, Bhadra P, Li A, Sethiya P, Qin L, Tai HK, et al. Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning. Mol Ther Nucleic Acids. 2020;20:882–94. [DOI] [PubMed] [PMC]

130.

Lin TT, Yang LY, Lu IH, Cheng WC, Hsu ZR, Chen S, et al. AI4AMP: an Antimicrobial Peptide Predictor Using Physicochemical Property-Based Encoding Method and Deep Learning. mSystems. 2021;6:e0029921. [DOI] [PubMed] [PMC]

131.

Fernández-Díaz R, Cossio-Pérez R, Agoni C, Lam HT, Lopez V, Shields DC. AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors. Bioinformatics. 2024;40:btae555. [DOI] [PubMed] [PMC]

132.

Bhadra P, Yan J, Li J, Fong S, Siu SWI. AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Sci Rep. 2018;8:1697. [DOI] [PubMed] [PMC]

133.

Xu J, Li F, Li C, Guo X, Landersdorfer C, Shen HH, et al. iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities. Brief Bioinform. 2023;24:bbad240. [DOI] [PubMed] [PMC]

134.

Wang Z, Meng J, Li H, Xia S, Wang Y, Luan Y. PAMPred: A hierarchical evolutionary ensemble framework for identifying plant antimicrobial peptides. Comput Biol Med. 2023;166:107545. [DOI] [PubMed]

135.

Zhang H, Saravanan KM, Wei Y, Jiao Y, Yang Y, Pan Y, et al. Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening. J Chem Inf Model. 2023;63:835–45. [DOI] [PubMed]

136.

Ghanbarzadeh Z, Hemmati S, Mohagheghzadeh A. Humanizing plant-derived snakins and their encrypted antimicrobial peptides. Biochimie. 2022;199:92–111. [DOI] [PubMed]

137.

Liu Y, Shen T, Chen L, Zhou J, Wang C. Analogs of the Cathelicidin-Derived Antimicrobial Peptide PMAP-23 Exhibit Improved Stability and Antibacterial Activity. Probiotics Antimicrob Proteins. 2021;13:273–86. [DOI] [PubMed]

138.

Lyu Y, Tan M, Xue M, Hou W, Yang C, Shan A, et al. Broad-spectrum hybrid antimicrobial peptides derived from PMAP-23 with potential LPS binding ability. Biochem Pharmacol. 2023;210:115500. [DOI] [PubMed]

139.

Schmidtchen A, Frick IM, Andersson E, Tapper H, Björck L. Proteinases of common pathogenic bacteria degrade and inactivate the antibacterial peptide LL-37. Mol Microbiol. 2002;46:157–68. [DOI] [PubMed]

140.

Gunasekera S, Muhammad T, Strömstedt AA, Rosengren KJ, Göransson U. Backbone Cyclization and Dimerization of LL-37-Derived Peptides Enhance Antimicrobial Activity and Proteolytic Stability. Front Microbiol. 2020;11:168. [DOI] [PubMed] [PMC]

141.

Wan F, Wong F, Collins JJ, de la Fuente-Nunez C. Machine learning for antimicrobial peptide identification and design. Nat Rev Bioeng. 2024;2:392–407. [DOI] [PubMed] [PMC]