Innovative Applications of Artificial Intelligence in Safety Assessment of Biological Breeding Products

doi:10.19586/j.2095-2341.2026.0032

Current Biotechnology ›› 2026, Vol. 16 ›› Issue (4): 765-773.DOI: 10.19586/j.2095-2341.2026.0032

• Special Forum on Detection Technology for Genetically Modified Organisms • Previous Articles Next Articles

Innovative Applications of Artificial Intelligence in Safety Assessment of Biological Breeding Products

Tianyi HAN¹(), Yu GAO¹, Nan CHENG¹, Xiaoyun HE¹, Weichen LIU², Wei FU³, Kunlun HUANG¹(), Hong CHEN³()

^1.College of Food Science and Nutritional Engineering，China Agricultural University，Beijing 100083，China
^2.Chinese Academy of Quality and Inspection & Testing，Beijing 100176，China
^3.Development Center for Science and Technology，Ministry of Agriculture and Rural Affairs，Beijing 100176，China

Received:2026-02-09 Accepted:2026-04-03 Online:2026-07-25 Published:2026-09-11
Contact: Kunlun HUANG,Hong CHEN

Abstract

Abstract:

Edible safety assessment of biologically breeding products is critical for preventing potential toxic and allergenic risks and protecting consumer safety. It is also indispensable for building consumer confidence and advancing the sustainable development of the industry. The rapid development of artificial intelligence （AI） has provided a brand-new technical path and method of support for the safety evaluation of biological breeding products. It not only enables non-destructive food testing but also significantly improves the accuracy and efficiency of the evaluation process. This article systematically reviewed the application progress of artificial intelligence in the edible safety evaluation of biological breeding products， such as genetically modified and gene-edited foods， from four aspects： multi-modal data sources， allergenicity and toxicological evaluation， nutritional evaluation， and adverse effect evaluation. The aim was to provide theoretical support for the more comprehensive application of artificial intelligence in edible safety evaluation.

Key words: genetically modified foods, biological breeding, artificial intelligence, biotechnology, safety assessment

CLC Number:

Tianyi HAN, Yu GAO, Nan CHENG, Xiaoyun HE, Weichen LIU, Wei FU, Kunlun HUANG, Hong CHEN. Innovative Applications of Artificial Intelligence in Safety Assessment of Biological Breeding Products[J]. Current Biotechnology, 2026, 16(4): 765-773.

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Figures/Tables 5

References 50

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应用领域	数据库/工具名称	主要特征与应用价值
食品营养学评估	USDA Food Data Central	美国农业部发布，包含标准参考、品牌及实验食品数据，是科研分析的首选
	中国食物成分表	针对中国本土饮食习惯，记录特有食材营养数据，更具地域适用性
	McCance and Widdowson's	英国/欧洲代表性数据库，数据质量极高，常用于跨国营养研究
	Phenol-Explorer	专注植物生化物质（多酚），记录加工过程（煎炸煮等）对含量的影响
致敏与毒性评估	AlphaFold	预测蛋白质三维结构，弥补晶体结构数据缺失
	WHO/IUIS & AllergenOnline	权威过敏原数据库，用于序列比对和风险关联分析
	MassBank / Metabolomics Workbench	公共质谱数据库，用于复杂基质中未知代谢物、过敏原及污染物的鉴定
非期望效应	KEGG / HMDB / FooDB	生物代谢通路及化合物库，用于识别非自然变异范围的成分变化

应用领域	数据库/工具名称	主要特征与应用价值
食品营养学评估	USDA Food Data Central	美国农业部发布，包含标准参考、品牌及实验食品数据，是科研分析的首选
	中国食物成分表	针对中国本土饮食习惯，记录特有食材营养数据，更具地域适用性
	McCance and Widdowson's	英国/欧洲代表性数据库，数据质量极高，常用于跨国营养研究
	Phenol-Explorer	专注植物生化物质（多酚），记录加工过程（煎炸煮等）对含量的影响
致敏与毒性评估	AlphaFold	预测蛋白质三维结构，弥补晶体结构数据缺失
	WHO/IUIS & AllergenOnline	权威过敏原数据库，用于序列比对和风险关联分析
	MassBank / Metabolomics Workbench	公共质谱数据库，用于复杂基质中未知代谢物、过敏原及污染物的鉴定
非期望效应	KEGG / HMDB / FooDB	生物代谢通路及化合物库，用于识别非自然变异范围的成分变化

评价对象	模型/工具名称	核心算法/技术	优势与性能表现
大分子蛋白质	AllergenAI	CNN（局部特征）+Bi-LSTM（长程依赖）	仅依赖序列即可量化致敏潜力，无需经验数据支撑
	AllerCatPro 2.0	结构同源建模(SWISS-MODEL)+线性表位滑动窗口	整合10个权威库，高灵敏度的同时降低25%假阳性率
	ToxinPred2/Toxify	机器学习/深度学习	欧盟食品安全局关注的工具，准确度与适用性较好
小分子物质	DeepTox	深度多任务学习(deep learning）	Tox21挑战赛优胜者，擅长预测核受体激活及应激反应
	ADMETlab 2.0	多任务图注意力框架(graph attention）	高效预测吸收、分布、代谢、排泄及毒性等80多个指标
	ProTox-Ⅱ	分子相似性+药效团+机器学习	快速筛查LD₅₀、肝毒性、致癌性及致突变性

评价对象	模型/工具名称	核心算法/技术	优势与性能表现
大分子蛋白质	AllergenAI	CNN（局部特征）+Bi-LSTM（长程依赖）	仅依赖序列即可量化致敏潜力，无需经验数据支撑
	AllerCatPro 2.0	结构同源建模(SWISS-MODEL)+线性表位滑动窗口	整合10个权威库，高灵敏度的同时降低25%假阳性率
	ToxinPred2/Toxify	机器学习/深度学习	欧盟食品安全局关注的工具，准确度与适用性较好
小分子物质	DeepTox	深度多任务学习(deep learning）	Tox21挑战赛优胜者，擅长预测核受体激活及应激反应
	ADMETlab 2.0	多任务图注意力框架(graph attention）	高效预测吸收、分布、代谢、排泄及毒性等80多个指标
	ProTox-Ⅱ	分子相似性+药效团+机器学习	快速筛查LD₅₀、肝毒性、致癌性及致突变性

检测目标	模型/研究案例	关键技术	改进效果/主要发现
宏量营养素	动植物源奶识别	高光谱成像(HSI) + 卷积神经网络（CNN）	耗时仅78 s，准确率达99.9%，实现快速无损检测
宏量营养素	Swin-Nutrition	近红外光谱（NIR）+分层视觉变换器（SwinT）	利用注意力机制捕获长程波段关联；蛋白质RMSE降低23.6%，优于传统CNN
抗营养因子	ANPS模型	理化特征（AAC/DPC）+支持向量机（SVM）	识别准确率94.31%，解决了低同源性蛋白难识别的问题
抗营养因子	Glycomol	超高性能液相色谱/高分辨率质谱（UPLC/HRMS）+图神经网络(GNN）	模拟糖链裂解反推结构，Top-1准确率88.4%，将解析时间从数天缩短至秒级

Innovative Applications of Artificial Intelligence in Safety Assessment of Biological Breeding Products

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评价层面	整合数据/技术	评价逻辑与突破
宏观预测与设计	生成式AI+气象大数据+AlphaFold	模拟G×E互作：预测极端气候下蛋白质折叠与代谢流向，早期干预代谢瓶颈与过敏风险
微观表型检测	高光谱表型组学+级联筛选	构象偏离：低成本精准捕捉种子内部蛋白质与淀粉的微观构象变化
元素代谢指纹	激光诱导击穿光谱+随机森林	元素异常：通过Mg/Ca/K光谱指纹，揭示转基因操作对矿物质代谢的非期望干扰
基因调控与预警	生成式AI+可解释AI(XAI）	蓝图设计：识别关键调控元件（CREs/uORFs），强调利用XAI解析非期望效应背后的分子机制

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