计算机辅助抗菌肽设计算法和工具的研究进展

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中图分类号：R978.1 文献标志码：A 文章编号：1001-8751（2025）05-0289-08

ResearchProgresson Computer-aidedAntimicrobial Peptide Design Algorithms and Tools

， ， ， （School ofLife Science and Technology，China Pharmaceutical University，Nanjing 210009）

Abstract： With the rise of antibiotic resistance，the efectiveness of traditional antibiotics is being challnged. Antimicrobial peptide （AMP），anovel classof antimicrobial drug，has demonstrated promising application prospects. Its diverse structureandbroad-spectrumantibacterial activity，as wellasitsability tocontrol the host immune system and effectively eradicate a varietyof pathogenic microorganisms，slow theemergence of drug resistance，and more. However，antimicrobial peptides alsohave some inherent defects，suchaslarge toxic side efects，poorstabilityand high synthesis and production costs.Therefore，there is stillaneed to designand develop novel antimicrobial peptides in order to overcome their shortcomings and promote their clinical application. In recent years，the development of genomics and metagenomics has provided alotof potentialresources forthe discoveryofantimicrobial peptides，and also promoted the research related to computer-aided antimicrobial peptide design. We addressthe sources，structures， antimicrobial mechanisms，databases，and prediction toolsassociated withAMPdesign inthisreview，whichfocuses on the use of machine learning and deep learning techniques in the feld ofAMP design.This willencourage further research and development ofAMP and serve as aresource for preventing antibiotic resistance and enhancing treatment effectiveness.

Key words： antimicrobial peptides; mechanism of action； databases; machine learning; deep learning; researchadvances

抗生素的广泛应用，引起了多重耐药病原体的出现，降低了传统抗生素的治疗效率[1-2]。（剩余16033字）