人工智能在DNA设计中的研究进展

doi:10.13560/j.cnki.biotech.bull.1985.2025-0863

生物技术通报 ›› 2026, Vol. 42 ›› Issue (1): 51-66.doi: 10.13560/j.cnki.biotech.bull.1985.2025-0863

人工智能在DNA设计中的研究进展

刘欢¹^,²(), 郭发旭³, 赵晓燕², 黄龙雨², 王健¹, 周国民⁴^,⁵^,⁶(), 张建华¹^,²()

^1.中国农业科学院农业信息研究所，北京 100081
^2.三亚中国农业科学院国家南繁研究院，三亚 572024
^3.甘肃农业大学机电工程学院，兰州 730070
^4.农业农村部南京农业机械化研究所，南京 210014
^5.国家农业科学数据中心，北京 100081
^6.中国农业科学院西部农业;研究中心，昌吉 831100

收稿日期:2025-08-09 出版日期:2026-01-26 发布日期:2026-02-04
通讯作者: 张建华，男，博士，研究员，研究方向：计算机视觉；E-mail: zhangjianhua@caas.cn；
周国民，男，博士，研究员，研究方向：信息技术与数字农业；E-mail: zhouguomin@caas.cn
作者简介:刘欢，男，硕士研究生，研究方向：数据分析；E-mail: liuhuan01@139.com
基金资助:
国家重点研发计划(2022YFF0711805);国家重点研发计划(2022YFF0711801);三亚中国农业科学院国家南繁研究院南繁专项(YBXM2409);三亚中国农业科学院国家南繁研究院南繁专项(YBXM2410);三亚中国农业科学院国家南繁研究院南繁专项(YBXM2312);三亚中国农业科学院国家南繁研究院南繁专项(ZDXM2311);中央级公益性科研院所基本科研业务费专项(JBYW-AII-2024-05);中央级公益性科研院所基本科研业务费专项(JBYW-AII-2023-06);中国农业科学院科技创新工程(CAAS-ASTIP-2024-AII);中国农业科学院科技创新工程(CAAS-ASTIP-2023-AII);三亚崖州湾科技城科技专项资助(SCKJ-JYRC-2023-45);海南省自然科学基金(325MS155)

Advances in Artificial Intelligence for DNA Design

LIU Huan¹^,²(), GUO Fa-xu³, ZHAO Xiao-yan², HUANG Long-yu², WANG Jian¹, ZHOU Guo-min⁴^,⁵^,⁶(), ZHANG Jian-hua¹^,²()

^1.Agricultural Information Institute，Chinese Academy of Agricultural Sciences，Beijing 100081
^2.National Nanfan Research Institute，Chinese Academy of Agriculture Science，Sanya 572024
^3.College of Mechanical and Electrical Engineering，Gansu Agricultural University，Lanzhou 730070
^4.Nanjing Institute of Agricultural Mechanization，Ministry of Agriculture and Rural Affairs，Nanjing 210014
^5.National Agricultural Science Data Center，Beijing 100081
^6.Institute of Western Agriculture，Chinese Academy of Agricultural Sciences，Changji 831100

Received:2025-08-09 Published:2026-01-26 Online:2026-02-04

摘要/Abstract

摘要：

DNA设计是根据特定功能需求定向构建与优化基因组功能元件，已成为合成生物学与精准育种等前沿领域的关键核心技术。传统设计方法受限于对复杂调控网络认知不足及序列搜索空间巨大等瓶颈，难以实现高效、精准的序列创新。近年来，人工智能技术，特别是深度生成模型与预测模型的融合应用，正深刻重塑DNA设计的理论基础与技术范式。该范式通过学习海量组学数据中蕴含的“调控语法”，能够在超长基因组序列背景下实现高分辨率的功能预测、多模态信息整合与条件可控的序列生成。本文系统综述了人工智能在DNA设计中的前沿进展，重点阐述了深度生成与预测模型在启动子、增强子等多层级调控元件设计、序列优化及作物育种等领域的关键技术路径与应用实例。通过构建“设计—预测—优化—验证”的智能化闭环，人工智能不仅显著提升了复杂功能元件的设计效率与准确性，更催生了性能超越天然元件的“超天然”序列。展望未来，随着人工智能与合成生物学、实验自动化等领域的深度融合，DNA设计有望实现从智能化设计到高通量实验验证的完整闭环，从而加速生命科学基础研究与现代农业育种等领域的创新突破。

关键词: 人工智能, DNA设计, 生成模型, 预测模型

Abstract:

DNA design, namely the targeted construction and optimization of genomic functional elements to meet specific performance requirements, has become a core enabling technology at the forefront of synthetic biology and precision breeding. Traditional design approaches are constrained by limited understanding of complex regulatory networks and the vastness of the sequence search space, making efficient and precise sequence innovation difficult. In recent years, artificial intelligence (AI), especially the integrated use of deep generative and predictive models, has been reshaping the theoretical foundations and technical paradigms of DNA design. By learning the "regulatory grammar" embedded in massive omics datasets, these methods enable high-resolution functional prediction, multimodal data integration, and condition-controlled sequence generation within ultra-long genomic contexts. This article systematically reviews cutting-edge advances in AI for DNA design, with an emphasis on key technical pathways and applications of deep generative and predictive models in the design of multi-level regulatory elements such as promoters and enhancers, sequence optimization, and crop breeding. By establishing an intelligent closed loop of “design–prediction–optimization–validation”, AI not only markedly improves the efficiency and accuracy of designing complex functional elements, but also gives rise to synthetic sequences that outperform their natural counterparts. Looking ahead, as AI further converges with synthetic biology and experimental automation, DNA design is poised to achieve a full pipeline from intelligent design to high-throughput experimental validation, thereby accelerating breakthroughs in basic life science research and modern agricultural breeding.

Key words: artificial intelligence, DNA design, generative model, predictive model

刘欢, 郭发旭, 赵晓燕, 黄龙雨, 王健, 周国民, 张建华. 人工智能在DNA设计中的研究进展[J]. 生物技术通报, 2026, 42(1): 51-66.

LIU Huan, GUO Fa-xu, ZHAO Xiao-yan, HUANG Long-yu, WANG Jian, ZHOU Guo-min, ZHANG Jian-hua. Advances in Artificial Intelligence for DNA Design[J]. Biotechnology Bulletin, 2026, 42(1): 51-66.

图/表 7

参考文献 50

[1]	Eraslan G, Avsec Ž, Gagneur J, et al. Deep learning: new computational modelling techniques for genomics [J]. Nat Rev Genet, 2019, 20(7): 389-403.
[2]	Kelley DR, Reshef YA, Bileschi M, et al. Sequential regulatory activity prediction across chromosomes with convolutional neural networks [J]. Genome Res, 2018, 28(5): 739-750.
[3]	Agarwal V, Shendure J. Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks [J]. Cell Rep, 2020, 31(7): 107663.
[4]	Zhang PC, Wang HC, Xu HW, et al. Deep flanking sequence engineering for efficient promoter design using DeepSEED [J]. Nat Commun, 2023, 14: 6309.
[5]	Wu MR, Nissim L, Stupp D, et al. A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS) [J]. Nat Commun, 2019, 10: 2880.
[6]	Yu TC, Liu WL, Brinck MS, et al. Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems [J]. Nat Commun, 2021, 12: 325.
[7]	Ji YR, Zhou ZH, Liu H, et al. DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome [J]. Bioinformatics, 2021, 37(15): 2112-2120.
[8]	Linder J, Bogard N, Rosenberg AB, et al. A generative neural network for maximizing fitness and diversity of synthetic DNA and protein sequences [J]. Cell Syst, 2020, 11(1): 49-62.e16.
[9]	Zhou ZH, Ji YR, Li WJ, et al. DNABERT-2: Efficient foundation model and benchmark for multi-species genomes [J]. arXiv.org. 2023. DOI: 10.48550/arxiv.2306.15006 .
[10]	Avsec Ž, Weilert M, Shrikumar A, et al. Base-resolution models of transcription-factor binding reveal soft motif syntax [J]. Nat Genet, 2021, 53(3): 354-366.
[11]	Wang Y, Wang HC, Wei L, et al. Synthetic promoter design in Escherichia coli based on a deep generative network [J]. Nucleic Acids Res, 2020, 48(12): 6403-6412.
[12]	Killoran N, Lee LJ, Delong A, et al. Generating and designing DNA with deep generative models [J]. arXiv.org. 2017. DOI: 10.48550/arxiv.1712.06148 .
[13]	Gupta A, Zou J. Feedback GAN for DNA optimizes protein functions [J]. Nat Mach Intell, 2019, 1(2): 105-111.
[14]	Wang XL, Xu KJ, Tan YM, et al. Deep learning-assisted design of novel promoters in Escherichia coli [J]. Adv Genet, 2023, 4(4): 2300184.
[15]	Alipanahi B, Delong A, Weirauch MT, et al. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning [J]. Nat Biotechnol, 2015, 33(8): 831-838.
[16]	Wang XL, Xu KJ, Huang ZS, et al. Accelerating promoter identification and design by deep learning [J]. Trends Biotechnol, 2025.
[17]	Barbadilla-Martínez L, Klaassen N, van Steensel B, et al. Predicting gene expression from DNA sequence using deep learning models [J]. Nat Rev Genet, 2025, 26(10): 666-680.
[18]	Quang D, Xie XH. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences [J]. Nucleic Acids Res, 2016, 44(11): e107.
[19]	Jores T, Tonnies J, Wrightsman T, et al. Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters [J]. Nat Plants, 2021, 7(6): 842-855.
[20]	Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model [J]. Nat Meth, 2015, 12(10): 931-934.
[21]	Kelley DR, Snoek J, Rinn JL. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks [J]. Genome Res, 2016, 26(7): 990-999.
[22]	Li ZH, Zhang YY, Peng B, et al. A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity [J]. Nucleic Acids Res, 2024, 52(21): 13447-13468.
[23]	Yin C, Castillo-Hair S, Byeon GW, et al. Iterative deep learning design of human enhancers exploits condensed sequence grammar to achieve cell-type specificity [J]. Cell Syst, 2025, 16(7): 101302.
[24]	Vaishnav ED, de Boer CG, Molinet J, et al. The evolution, evolvability and engineering of gene regulatory DNA [J]. Nature, 2022, 603(7901): 455-463.
[25]	Fu HG, Liang YB, Zhong XQ, et al. Codon optimization with deep learning to enhance protein expression [J]. Sci Rep, 2020, 10: 17617.
[26]	Yang G, Chen YJ, Guo QH, et al. Leveraging pre-trained AI models for robust promoter sequence design in synthetic biology [J]. Swwlxb, 2025, 11: 1.
[27]	Fallahpour A, Gureghian V, Filion GJ, et al. CodonTransformer: a multispecies codon optimizer using context-aware neural networks [J]. Nat Commun, 2025, 16: 3205.
[28]	Zrimec J, Fu XZ, Muhammad AS, et al. Controlling gene expression with deep generative design of regulatory DNA [J]. Nat Commun, 2022, 13: 5099.
[29]	de Almeida BP, Schaub C, Pagani M, et al. Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo. [J]. Nature, 2024, 626(7997): 207-211.
[30]	Avdeyev P, Shi CL, Tan YH, et al. Dirichlet diffusion score model for biological sequence generation [J]. arXiv.org. 2023. DOI: 10.48550/arxiv.2305.10699 .
[31]	de Almeida BP, Reiter F, Pagani M, et al. DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [J]. Nat Genet, 2022, 54(5): 613-624.
[32]	Avsec Ž, Agarwal V, Visentin D, et al. Effective gene expression prediction from sequence by integrating long-range interactions [J]. Nat Meth, 2021, 18(10): 1196-1203.
[33]	Yang Y, Lee JH, Poindexter MR, et al. Rational design and testing of abiotic stress-inducible synthetic promoters from poplar cis-regulatory elements [J]. Plant Biotechnol J, 2021, 19(7): 1354-1369.
[34]	Jain R, Jain A, Mauro E, et al. ICOR: improving codon optimization with recurrent neural networks [J]. BMC Bioinform, 2023, 24(1): 132.
[35]	Lei X, Wang X, Chen GL, et al. Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning [J]. mSystems, 2025, 10(4)
[36]	Kelley DR. Cross-species regulatory sequence activity prediction [J]. PLoS Comput Biol, 2020, 16(7): e1008050.
[37]	Li JQ, Zhang PC, Xi X, et al. Modeling and designing enhancers by introducing and harnessing transcription factor binding units [J]. Nat Commun, 2025, 16: 1469.
[38]	Gasperini M, Tome JM, Shendure J. Towards a comprehensive catalogue of validated and target-linked human enhancers [J]. Nat Rev Genet, 2020, 21(5): 292-310.
[39]	Zhou J, Theesfeld CL, Yao K, et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk [J]. Nat Genet, 2018, 50(8): 1171-1179.
[40]	Friedman RZ, Ramu A, Lichtarge S, et al. Active learning of enhancers and silencers in the developing neural retina [J]. Cell Syst, 2025, 16(1): 101163.
[41]	Karbalayghareh A, Sahin M, Leslie CS. Chromatin interaction-aware gene regulatory modeling with graph attention networks [J]. Genome Res, 2022, 32(7): 1290-1304.
[42]	Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, et al. Predicting splicing from primary sequence with deep learning [J]. Cell, 2019, 176(3): 535-548.e24.
[43]	Sample PJ, Wang B, Reid DW, et al. Human 5' UTR design and variant effect prediction from a massively parallel translation assay [J]. Nat Biotechnol, 2019, 37(7): 803-809.
[44]	Bogard N, Linder J, Rosenberg AB, et al. A deep neural network for predicting and engineering alternative polyadenylation [J]. Cell, 2019, 178(1): 91-106.e23.
[45]	Lee NK, Tang ZQ, Toneyan S, et al. EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations [J]. Genome Biol, 2023, 24(1): 105.
[46]	Cherednichenko O, Poptsova M. Data augmentation with generative models improves detection of Non-B DNA structures [J]. Comput Biol Med, 2025, 184: 109440.
[47]	Davidi D, et al. Regulatory DNA sequence design with reinforcement learning [DB/OL]. arXiv preprint: 2503.07981.
[48]	Jaganathan K, Ersaro N, Novakovsky G, et al. Predicting expression-altering promoter mutations with deep learning [J]. Science, 2025, 389(6760): eads7373.
[49]	Dalla-Torre H, Gonzalez L, Mendoza-Revilla J, et al. Nucleotide Transformer: building and evaluating robust foundation models for human genomics [J]. Nat Meth, 2025, 22(2): 287-297.
[50]	张冀东, 王志晗, 刘博, 等. 深度学习在生物序列分析领域的应用进展 [J]. 北京工业大学学报, 2022, 48(8): 878-887.
	Zhang JD, Wang ZH, Liu B, et al. Progress in the applications of deep learning in biological sequence analysis [J]. J Beijing Univ Technol, 2022, 48(8): 878-887.

模型 Model	架构类型 Architecture type	主要用途 Primary applications	特点 Features	优势 Advantages	局限 Limitations
Enformer	Transformer	功能预测	长程依赖	泛化性强	数据需求量大
DNABERT	Transformer	序列特征提取	k-mer嵌入	泛化能力好	解释性有限
Evo/Evo2	Transformer	序列设计/预测	跨模态泛化生	零样本迁移	复杂度较高
GAN	对抗生成网络	序列设计/预测	成创新序列	功能多样	训练稳定性差
VAE	自编码器	序列优化	潜在空间优化	控制性强	泛化较难
Diffusion	扩散模型	序列精准生成	稳定性强	可控性强	计算资源需求高

模型 Model	架构类型 Architecture type	主要用途 Primary applications	特点 Features	优势 Advantages	局限 Limitations
Enformer	Transformer	功能预测	长程依赖	泛化性强	数据需求量大
DNABERT	Transformer	序列特征提取	k-mer嵌入	泛化能力好	解释性有限
Evo/Evo2	Transformer	序列设计/预测	跨模态泛化生	零样本迁移	复杂度较高
GAN	对抗生成网络	序列设计/预测	成创新序列	功能多样	训练稳定性差
VAE	自编码器	序列优化	潜在空间优化	控制性强	泛化较难
Diffusion	扩散模型	序列精准生成	稳定性强	可控性强	计算资源需求高

数据库/资源 Database/Resource	内容简介 Description	类型/范围 Type/Scope	用途 Applications
GENCODE	人类注释基因组	注释序列	基因注释
INSD	核酸数据集	序列库	通用序列建模
EPDnew	启动子信息	启动子功能	功能预测
EnhancerAtlas	增强子注释	增强子库	功能研究
MethBank	甲基化数据	表观遗传	调控分析
UCSC Browser	多组学数据	组学集成	多组学分析
GEO	转录组表观组	组学数据库	转录组分析
ENCODE	功能元件注释数据	多组学	功能元件注释
Roadmap	表观遗传组	表观组	功能研究
GTEx	组织表达图谱	转录组	表达分析
Ensembl	多物种注释	基因组注释	跨物种建模
RNAcentral	ncRNA库	非编码RNA	序列分析
GreeNC	植物ncRNA	植物数据库	功能元件设计
DBHR	多组学整合	综合组学	功能研究
UniProt	蛋白功能库	蛋白注释	功能预测
MPRA	高通量实验	功能平台	功能筛选

数据库/资源 Database/Resource	内容简介 Description	类型/范围 Type/Scope	用途 Applications
GENCODE	人类注释基因组	注释序列	基因注释
INSD	核酸数据集	序列库	通用序列建模
EPDnew	启动子信息	启动子功能	功能预测
EnhancerAtlas	增强子注释	增强子库	功能研究
MethBank	甲基化数据	表观遗传	调控分析
UCSC Browser	多组学数据	组学集成	多组学分析
GEO	转录组表观组	组学数据库	转录组分析
ENCODE	功能元件注释数据	多组学	功能元件注释
Roadmap	表观遗传组	表观组	功能研究
GTEx	组织表达图谱	转录组	表达分析
Ensembl	多物种注释	基因组注释	跨物种建模
RNAcentral	ncRNA库	非编码RNA	序列分析
GreeNC	植物ncRNA	植物数据库	功能元件设计
DBHR	多组学整合	综合组学	功能研究
UniProt	蛋白功能库	蛋白注释	功能预测
MPRA	高通量实验	功能平台	功能筛选

位置 Position	A密度 A density	C密度 C density	G密度 G density	T密度 T density
1	1.00	0.00	0.00	0.00
2	0.50	0.00	0.50	0.00
3	0.33	0.00	0.33	0.33
4	0.25	0.25	0.25	0.25
5	0.20	0.20	0.20	0.10
6	0.17	0.17	0.33	0.33
7	0.14	0.29	0.29	0.29
8	0.25	0.25	0.25	0.25

人工智能在DNA设计中的研究进展

Advances in Artificial Intelligence for DNA Design

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