摘要
现代农业发展对新农药创制提出了更高要求,然而传统研发模式在效率、成本控制及环境相容性等方面面临显著瓶颈。随着人工智能(AI)技术的突破性进展,大语言模型等AI技术为农药创新研究开辟了智能化新路径,展现出巨大的应用潜力。系统探讨了DeepSeek大模型在农药分子设计中的创新应用及其技术优势。通过构建垂直领域知识库、多模态数据整合和动态交互系统,DeepSeek实现了对农药专业术语的精准解析、化学结构与生物活性的智能关联以及跨语言专利检索的无缝衔接。案例研究表明,其在对吲唑虫酰胺类新化学农药的改造中展现出对现有农药改造方案的学习和整合能力,在新骨架创制、逆合成分析、农药性质汇总和活性及内吸性预测中也能体现出一定优势。DeepSeek通过融合生成对抗网络与量子化学约束的多目标优化框架,并且加入了DeepSeek-chem专业模式,显著增大了有关农药分子设计方向的检索范围且提升了检索效率。揭示了人工智能技术在破解农药研发中环境友好性与生物活性协同优化等关键难题时的突破性价值,为农药创制范式向智能化转型提供了创新解决方案。
The evolving landscape of agricultural development demands innovative approaches to pesticide discovery,yet traditional research and development models encounter significant limitations in efficiency,cost-effectiveness,and environmental sustainability.Recent breakthroughs in artificial intelligence(AI),particularly large language models,have unveiled intelligent pathways for pesticide innovation research,demonstrating substantial application potential.This study systematically examines the innovative applications and technological advantages of the DeepSeek large model in pesticide molecular design.By establishing domain-specific knowledge bases,integrating multi-modal data,and developing dynamic interactive systems,DeepSeek enables precise parsing of pesticide terminology,intelligent correlation of chemical structures with biological activity,and seamless cross-language patent retrieval.Empirical case studies substantiate the model's capacity to learn and synthesize existing pesticide modification strategies,with notable advancements in transforming novel chemical pesticides like indazapyroxamet.The model exhibits particular strengths in generating molecular scaffolds,conducting reverse synthetic analysis,summarizing pesticide properties,and predicting biological activity and systemic mobility.By integrating a multi-objective optimization framework that combines generative adversarial networks with quantum chemical constraints and incorporating the DeepSeek-chem professional mode,the approach significantly expands search capabilities and enhances retrieval efficiency in pesticide molecular design.The research illuminates the transformative potential of AI in addressing critical challenges of environmentally compatible and biologically active synergistic optimization in pesticide research and development,thereby providing innovative solutions for the intelligent redesign of pesticide creation paradigms.
作者
马昀皓
陈铃诗
徐汉虹
MA Yunhao;CHEN Lingshi;XU Hanhong(South China Agricultural University,College of Plant Protection,Guangzhou 510642,China;South China Agricultural University,Library of South China Agricultural University,Guangzhou 510642,China)
出处
《农药》
北大核心
2025年第12期859-867,共9页
Agrochemicals
关键词
DeepSeek
农药设计
大语言模型
人工智能
实际应用
DeepSeek
pesticide design
large language model
artificial intelligence
practical application
