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本文综述抗病毒肽的来源及其在病毒感染多环节中的干预机制；重点阐述人工智能在抗病毒肽预测与设计中的前沿进展，包括深度神经网络、图神经网络、对比学习和生成式模型等技术突破；针对其体内半衰期短、口服生物利用度低、合成成本高等临床转化瓶颈，提出构建融合结构化数据管理与语义推理能力的AI知识库辅助研究，并简述构建方法，旨在为研究者提供系统的研究背景与明确的方向指引。
Abstract：In this paper, summarization was made for the sources of antiviral peptides(AVPs) and their intervention mechanisms functioning in multiple stages of viral infection. The cutting-edge progress regarding the prediction and design of AVPs aided by artificial intelligence(AI) was elaborated, including technical breakthroughs of deep neural networks, graph neural networks, contrastive learning and generative models. Given prominent bottlenecks restricting clinical translation of AVPs, namely short in vivo half-life, low oral bioavailability and high synthetic cost, it was proposed to build an AI knowledge base integrating structured data management and semantic reasoning to assist relevant researches, and the specific construction methods were briefly described. This aims to provide researchers with a comprehensive theoretical background and clear strategic directions for future AVP development.
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基本信息:
中图分类号:Q939.9
引用信息:
[1]温乔淞,张继伟,裴志花.抗病毒肽及其在AI时代下的研究进展[J].病毒学报().
基金信息:
吉林省自然科学基金（项目号：YDZJ202501ZYTS533,新型环化抗菌肽C-LR18在IPEG-J2细胞内抗猪流行性腹泻病毒的作用机制研究）
2026-09-04
2026-09-04
2026-09-04