Artificial Intelligence in Medicinal Herb Breeding

Biyu Hou; Caiyan Liang; Xiao Sheng; YongGuo Liu; JianDong Ren; Qiang Ma; Tengjiao Wang; Lei Zhang

doi:10.1016/j.eng.2025.08.021

Engineering ›› DOI: 10.1016/j.eng.2025.08.021

review-article

Artificial Intelligence in Medicinal Herb Breeding

Author information +

History +

PDF (1728KB)

Abstract

Medicinal plant-derived bioactive compounds serve as a crucial foundation for natural pharmaceutical development. Contemporary breeding methodologies can boost both the quality and yield of these medicinally valuable compounds, thereby advancing the development of the pharmaceutical sector. However, conventional breeding encounters substantial challenges when addressing the intricate genetic architectures and polygenic regulatory networks characteristic of medicinal species. These traditional modalities are especially ineffective in optimizing multiple pharmacologically relevant traits while maintaining robust environmental adaptability across diverse cultivation conditions concurrently. Advanced computational tools are emerging for biological research with parallel development of artificial intelligence (AI), which have also been explored for their applications in medicinal plant breeding. In the current comprehensive review, we carried out a systematic examination of the state-of-the-art AI applications across different aspects of the breeding pipeline, encompassing multi-omics data integration, synthetic biology, precision gene editing, trait optimization, and intelligent monitoring systems. Meanwhile, this review elucidated current obstacles of data integration, model generalization, and environmental adaptation when applying AI in medicinal plants, and proposed a concept of constructing a genotype–environment–management (G × E × M) interactive intelligent breeding platform. The integration of AI with biotechnology emphasizes data-driven precision, computational analysis, and potential for trait customization, contributing to shaping new approaches in medicinal plant breeding gradually. Collectively, these developments may facilitate profound improvements in breeding efficiency, compound yield, and environmental sustainability.

Keywords

Medicinal plants / Precision breeding / Artificial intelligence / Multi-omics integration / Genotype–phenotype prediction

Cite this article

Download citation ▾

Biyu Hou, Caiyan Liang, Xiao Sheng, YongGuo Liu, JianDong Ren, Qiang Ma, Tengjiao Wang, Lei Zhang. Artificial Intelligence in Medicinal Herb Breeding. Engineering DOI:10.1016/j.eng.2025.08.021

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Zhang C, Yang Z, Tang D, Zhu Y, Wang P, Li D, et al.Genome design of hybrid potato.Cell 2021; 184(15):3873-3883.e12.

[2]	Sasse A, Chikina M, Mostafavi S.Unlocking gene regulation with sequence-to-function models.Nat Methods 2024; 21(8):1374-1377.

[3]	Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, et al.Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.Mol Plant 2022; 15(11):1664-1695.

[4]	Simon E, Swanson K, Zou J.Language models for biological research: a primer.Nat Methods 2024; 21(8):1422-1429.

[5]	Zheng S, Zeng T, Li C, Chen B, Coley CW, Yang Y, et al.Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP.Nat Commun 2022; 13(1):3342.

[6]	Yu Y, Cheng Q, Wang F, Zhu Y, Shang X, Jones A, et al.Crop/plant modeling supports plant breeding: i. optimization of environmental factors in accelerating crop growth and development for speed breeding.Plant Phenomics 2023; 5:0099.

[7]	Yan J, Wang X.Machine learning bridges omics sciences and plant breeding.Trends Plant Sci 2023; 28(2):199-210.

[8]	Zhong C, Chen C, Gao X, Tan C, Bai H, Ning K.Multi-omics profiling reveals comprehensive microbe-plant-metabolite regulation patterns for medicinal plant Glycyrrhiza uralensis Fisch.Plant Biotechnol J 2022; 20(10):1874-1887.

[9]	Liu H, Lu X, Hu Y, Fan X.Chemical constituents of Panax ginseng and Panax notoginseng explain why they differ in therapeutic efficacy.Pharmacol Res 2020; 161:105263.

[10]	Qian Y, Zheng Y, Jin J, Wu X, Xu K, Dai M, et al.Immunoregulation in diabetic wound repair with a photoenhanced glycyrrhizic acid hydrogel scaffold.Adv Mater 2022; 34(29):e2200521.

[11]	Masci A, Carradori S, Casadei MA, Paolicelli P, Petralito S, Ragno R, et al.Lycium barbarum polysaccharides: extraction, purification, structural characterisation and evidence about hypoglycaemic and hypolipidaemic effects. A review.Food Chem 2018; 254:377-389.

[12]	Pei Y, Leng L, Sun W, Liu B, Feng X, Li X, et al.Whole-genome sequencing in medicinal plants: current progress and prospect.Sci China Life Sci 2024; 67(2):258-273.

[13]	Sura MB, Cheng YX.Medicinal plant resin natural products: structural diversity and biological activities.Nat Prod Rep 2024; 41(10):1471-1542.

[14]	Teixidor-Toneu I, Jordan FM, Hawkins JA.Comparative phylogenetic methods and the cultural evolution of medicinal plant use.Nat Plants 2018; 4(10):754-761.

[15]	Zheng H, Fu X, Shao J, Tang Y, Yu M, Li L, et al.Transcriptional regulatory network of high-value active ingredients in medicinal plants.Trends Plant Sci 2023; 28(4):429-446.

[16]	Dauparas J, Anishchenko I, Bennett N, Bai H, Ragotte RJ, Milles LF, et al.Robust deep learning-based protein sequence design using ProteinMPNN.Science 2022; 378(6615):49-56.

[17]	Crossa J, P Pérez-Rodríguez, Cuevas J, Montesinos-López O, Jarquín D, de CGLos, et al.Genomic selection in plant breeding: methods, models, and perspectives.Trends Plant Sci 2017; 22(11):961-975.

[18]	Wang P, Lehti-Shiu MD, Lotreck S, Segura KAbá, Krysan PJ, Shiu SH.Prediction of plant complex traits via integration of multi-omics data.Nat Commun 2024; 15(1):6856.

[19]	Li W, Jiang X, Wang W, Hou L, Cai R, Li Y, et al.Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models.Nat Commun 2024; 15(1):10024.

[20]	Kun W, Shoupu H, Yuxian Z.Cotton2035: from genomics research to optimized breeding.Mol Plant 2025; 18(2):298-312.

[21]	Farooq MA, Gao S, Hassan MA, Huang Z, Rasheed A, Hearne S, et al.Artificial intelligence in plant breeding.Trends Genet 2024; 40(10):891-908.

[22]	Cui Y, Li R, Li G, Zhang F, Zhu T, Zhang Q, et al.Hybrid breeding of rice via genomic selection.Plant Biotechnol J 2020; 18(1):57-67.

[23]	Tong H, Nikoloski Z.Machine learning approaches for crop improvement: leveraging phenotypic and genotypic big data.J Plant Physiol 2021; 257:153354.

[24]	Ma W, Qiu Z, Song J, Li J, Cheng Q, Zhai J, et al.A deep convolutional neural network approach for predicting phenotypes from genotypes.Planta 2018; 248(5):1307-1318.

[25]	Zingaretti LM, Gezan SA, Ferr LFVão, Osorio LF, Monfort A, Muñoz PR, et al.Exploring deep learning for complex trait genomic prediction in polyploid outcrossing species.Front Plant Sci 2020; 11:25.

[26]	Yan J, Xu Y, Cheng Q, Jiang S, Wang Q, Xiao Y, et al.LightGBM: accelerated genomically designed crop breeding through ensemble learning.Genome Biol 2021; 22(1):271.

[27]	Westhues CC, Mahone GS, da SSilva, Thorwarth P, Schmidt M, Richter JC, et al.Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks.Front Plant Sci 2021; 12:699589.

[28]	Wang S, Wei J, Li R, Qu H, Chater JM, Ma R, et al.Identification of optimal prediction models using multi-omic data for selecting hybrid rice.Heredity 2019; 123(3):395-406.

[29]	Wang S, Liang W, Yao L, Wang J, Gao W.Effect of temperature on morphology, ginsenosides biosynthesis, functional genes, and transcriptional factors expression in Panax ginseng adventitious roots.J Food Biochem 2019; 43(4):e12794.

[30]	Wu D, Xiong F, Wang H, Liu S, Zhu J, Zhao D, et al.Temperature seasonality and soil phosphorus availability shape ginseng quality via regulating ginsenoside contents.BMC Plant Biol 2024; 24(1):824.

[31]	Mansoor S, Hamid S, Tuan TT, Park JE, Chung YS.Advance computational tools for multiomics data learning.Biotechnol Adv 2024; 77:108447.

[32]	Sayers EW, Bolton EE, Brister JR, Canese K, Chan J, Comeau DC, et al.Database resources of the national center for biotechnology information.Nucleic Acids Res 2022; 50(D1):D20-D26.

[33]	Bai X, Bao Y, Bei S, Bu C, Cao R, Cao Y, et al.Database Resources of the National Genomics Data Center. China national center for bioinformation in 2024.Nucleic Acids Res 2024; 52(D1):D18-D32.

[34]	Zhang N, Tang L, Li S, Liu L, Gao M, Wang S, et al.Integration of multi-omics data accelerates molecular analysis of common wheat traits.Nat Commun 2025; 16(1):2200.

[35]	Chen T, Yang M, Cui G, Tang J, Shen Y, Liu J, et al.IMP: bridging the gap for medicinal plant genomics.Nucleic Acids Res 2024; 52(D1):D1347-D1354.

[36]	He S, Yang L, Ye S, Lin Y, Li X, Wang Y, et al.MPOD: applications of integrated multi-omics database for medicinal plants.Plant Biotechnol J 2022; 20(5):797-799.

[37]	Liu D, Luo C, Dai R, Huang X, Chen X, He L, et al.AMIR: a multi-omics data platform for Asteraceae plants genetics and breeding research.Nucleic Acids Res 2025; 53(D1):D1563-D1575.

[38]	Kong X, Liu C, Zhang Z, Cheng M, Mei Z, Li X, et al.BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins.Nucleic Acids Res 2024; 52(D1):D1110-D1120.

[39]	Chen W, Yu Z, Leng L, Sun D, Liu H, Gong RZ, et al.Artificial intelligence-curated repository of gene-encoded natural diverse components from herbal medicines. Innovation (2025), Article 101011

[40]	Chandrasekhar V, Rajan K, Sri RSK, Sharma N, Wei Vßenborn, Schaub J, et al.COCONUT 2.0: a comprehensive overhaul and curation of the collection of open natural products database.Nucleic Acids Res 2025; 53(D1):D634-D643.

[41]	Fang S, Dong L, Liu L, Guo J, Zhao L, Zhang J, et al.HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine.Nucleic Acids Res 2021; 49(D1):D1197-D1206.

[42]	Tian S, Zhang J, Yuan S, Wang Q, Lv C, Wang J, et al.Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM.Brief Bioinform 2023; 24(2):bbad027.

[43]	Meng F, Tang Q, Chu T, Li X, Lin Y, Song X, et al.TCMPG: an integrative database for traditional Chinese medicine plant genomes.Hortic Res 2022; 9:uhac060.

[44]	Mendez D, Gaulton A, Bento AP, Chambers J, De MVeij, F Eélix, et al.ChEMBL: towards direct deposition of bioassay data.Nucleic Acids Res 2019; 47(D1):D930-D940.

[45]	Zeng X, Zhang P, Wang Y, Qin C, Chen S, He W, et al.CMAUP: a database of collective molecular activities of useful plants.Nucleic Acids Res 2019; 47(D1):D1118-D1127.

[46]	Yan D, Zheng G, Wang C, Chen Z, Mao T, Gao J, et al.HIT 2.0: an enhanced platform for Herbal Ingredients’ Targets.Nucleic Acids Res 2022; 50(D1):D1238-D1243.

[47]	Zheng CJ, Han LY, Xie B, Liew CY, Ong S, Cui J, et al.PharmGED: pharmacogenetic effect database.Nucleic Acids Res 2007; 35(Database issue):D794-D799.

[48]	Lv Q, Chen G, He H, Yang Z, Zhao L, Zhang K, et al.TCMBank-the largest TCM database provides deep learning-based Chinese–Western medicine exclusion prediction.Signal Transduct Target Ther 2023; 8(1):127.

[49]	Jin J, Tian F, Yang DC, Meng YQ, Kong L, Luo J, et al.PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants.Nucleic Acids Res 2017; 45(D1):D1040-D1045.

[50]	Lescot M, D Péhais, Thijs G, Marchal K, Moreau Y, Van Yde Peer, et al.PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences.Nucleic Acids Res 2002; 30(1):325-327.

[51]	Cheng LT, Wang ZL, Zhu QH, Ye M, Ye CY.A long road ahead to reliable and complete medicinal plant genomes.Nat Commun 2025; 16(1):2150.

[52]	Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al.The protein data bank.Nucleic Acids Res 2000; 28(1):235-242.

[53]	Dyer SC, Austine-Orimoloye O, Azov AG, Barba M, Barnes I, Barrera-Enriquez VP, et al.Ensembl 2025.Nucleic Acids Res 2025; 53(D1):D948-D957.

[54]	Thakur M, Buniello A, Brooksbank C, Gurwitz KT, Hall M, Hartley M, et al.EMBL’s european bioinformatics institute (EMBL-EBI) in 2023.Nucleic Acids Res 2024; 52(D1):D10-D17.

[55]	Li Z, Jiang X, Fang M, Bai Y, Liu S, Huang S, et al.CMDB: the comprehensive population genome variation database of China.Nucleic Acids Res 2023; 51(D1):D890-D895.

[56]	Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, et al.Phytozome: a comparative platform for green plant genomics.Nucleic Acids Res 2012; 40(Database issue):D1178-D1186.

[57]	Armit C, Tuli MA, Hunter CI.A decade of GigaScience: GigaDB and the open data movement.Gigascience 2022; 11:giac053.

[58]	Lan Y, Sun R, Ouyang J, Ding W, Kim MJ, Wu J, et al.AtMAD: arabidopsis thaliana multi-omics association database.Nucleic Acids Res 2021; 49(D1):D1445-D1451.

[59]	Dao F, Xie X, Zhang H, Guan Z, Wu C, Su W, et al.PlantEMS: a comprehensive database of epigenetic modification sites across multiple plant species.Plant Commun 2024; 6(4):101228.

[60]	Huang W, Hu X, Ren Y, Song M, Ma C, Miao Z.IPOP: an integrative plant multi-omics platform for cross-species comparison and evolutionary study.Mol Biol Evol 2023; 40(12):msad248.

[61]	Liu H, Wang X, Liu S, Huang Y, Guo YX, Xie WZ, et al.Citrus Pan-Genome to Breeding Database (CPBD): a comprehensive genome database for citrus breeding.Mol Plant 2022; 15(10):1503-1505.

[62]	Jung S, Lee T, Cheng CH, Buble K, Zheng P, Yu J, et al.15 years of GDR: New data and functionality in the Genome Database for Rosaceae.Nucleic Acids Res 2019; 47(D1):D1137-D1145.

[63]	Li J, Chen C, Zeng Z, Wu F, Feng J, Liu B, et al.SapBase: a central portal for functional and comparative genomics of Sapindaceae species.J Integr Plant Biol 2024; 66(8):1561-1570.

[64]	Chen H, Wang T, He X, Cai X, Lin R, Liang J, et al.BRAD V3.0: an upgraded Brassicaceae database.Nucleic Acids Res 2022; 50(D1):D1432-D1441.

[65]	Chen Y, Yang Z, Chen J, Li P, Zhao X, Huang S, et al.MagnoliidsGDB: an integrated functional genomics database for magnoliids.Plant Commun 2025; 6(5):101263.

[66]	Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M, Gil L, et al.The NHGRI-EBI GWAS catalog: knowledgebase and deposition resource.Nucleic Acids Res 2023; 51(D1):D977-D985.

[67]	Cooper L, Meier A, Laporte MA, Elser JL, Mungall C, Sinn BT, et al.The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics.Nucleic Acids Res 2018; 46(D1):D1168-D1180.

[68]	Danilevicz MF, Bayer PE, Nestor BJ, Bennamoun M, Edwards D.Resources for image-based high-throughput phenotyping in crops and data sharing challenges.Plant Physiol 2021; 187(2):699-715.

[69]	Gupta P, Elser J, Hooks E, D P’Eustachio, Jaiswal P, Naithani S.Plant Reactome Knowledgebase: empowering plant pathway exploration and OMICS data analysis.Nucleic Acids Res 2024; 52(D1):D1538-D1547.

[70]	Schwacke R, Ponce-Soto GY, Krause K, Bolger AM, Arsova B, Hallab A, et al.MapMan4: a refined protein classification and annotation framework applicable to multi-omics data analysis.Mol Plant 2019; 12(6):879-892.

[71]	Wang X, Liang S, Yang W, Yu K, Liang F, Zhao B, et al.MetMiner: a user-friendly pipeline for large-scale plant metabolomics data analysis.J Integr Plant Biol 2024; 66(11):2329-2345.

[72]	Rhee SY, Beavis W, Berardini TZ, Chen G, Dixon D, Doyle A, et al.The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community.Nucleic Acids Res 2003; 31(1):224-228.

[73]	Gyori BM, Vitek O.Beyond protein lists: AI-assisted interpretation of proteomic investigations in the context of evolving scientific knowledge.Nat Methods 2024; 21(8):1387-1389.

[74]	Depuydt T, De BRybel, Vandepoele K.Charting plant gene functions in the multi-omics and single-cell era.Trends Plant Sci 2023; 28(3):283-296.

[75]	Cao ZJ, Gao G.Multi-omics single-cell data integration and regulatory inference with graph-linked embedding.Nat Biotechnol 2022; 40(10):1458-1466.

[76]	Wang K, Abid MA, Rasheed A, Crossa J, Hearne S, Li H.DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants.Mol Plant 2023; 16(1):279-293.

[77]	Zhao L, Tang P, Luo J, Liu J, Peng X, Shen M, et al.Genomic prediction with NetGP based on gene network and multi-omics data in plants.Plant Biotechnol J 2025; 23(4):1190-1201.

[78]	Ren Y, Wu C, Zhou H, Hu X, Miao Z.Dual-extraction modeling: a multi-modal deep-learning architecture for phenotypic prediction and functional gene mining of complex traits.Plant Commun 2024; 5(9):101002.

[79]	Sza Ałata, Hrovatin K, Becker S, Tejada-Lapuerta A, Cui H, Wang B, et al.Transformers in single-cell omics: a review and new perspectives.Nat Methods 2024; 21(8):1430-1443.

[80]	Coleman K, Schroeder A, Li M.Unlocking the power of spatial omics with AI.Nat Methods 2024; 21(8):1378-1381.

[81]	Cameron DE, Bashor CJ, Collins JJ.A brief history of synthetic biology.Nat Rev Microbiol 2014; 12(5):381-390.

[82]	Montaño J López, Avalos JL.Genetically engineered yeast makes medicinal plant products.Nature 2020; 585(7826):504-505.

[83]	Wang Y, Demirer GS.Synthetic biology for plant genetic engineering and molecular farming.Trends Biotechnol 2023; 41(9):1182-1198.

[84]	Jones TS, Oliveira SMD, Myers CJ, Voigt CA, Densmore D.Genetic circuit design automation with Cello 2.0.Nat Protoc 2022; 17(4):1097-1113.

[85]	Chen Z, Elowitz MB.Programmable protein circuit design.Cell 2021; 184(9):2284-2301.

[86]	Chen Y, Zhang S, Young EM, Jones TS, Densmore D, Voigt CA.Genetic circuit design automation for yeast.Nat Microbiol 2020; 5(11):1349-1360.

[87]	Kong C, Yang Y, Qi T, Zhang S.Predictive genetic circuit design for phenotype reprogramming in plants.Nat Commun 2025; 16(1):715.

[88]	Zhang D, Xu F, Wang F, Le L, Pu L.Synthetic biology and artificial intelligence in crop improvement.Plant Commun 2025; 6(2):101220.

[89]	Wang Y, Wang H, Wei L, Li S, Liu L, Wang X.Synthetic promoter design in Escherichia coli based on a deep generative network.Nucleic Acids Res 2020; 48(12):6403-6412.

[90]	Zhang P, Wang H, Xu H, Wei L, Liu L, Hu Z, et al.Deep flanking sequence engineering for efficient promoter design using DeepSEED.Nat Commun 2023; 14(1):6309.

[91]	Seo E, Choi YN, Shin YR, Kim D, Lee JW.Design of synthetic promoters for cyanobacteria with generative deep-learning model.Nucleic Acids Res 2023; 51(13):7071-7082.

[92]	Xia Y, Du X, Liu B, Guo S, Huo YX.Species-specific design of artificial promoters by transfer-learning based generative deep-learning model.Nucleic Acids Res 2024; 52(11):6145-6157.

[93]	Li Z, Zhang Y, Peng B, Qin S, Zhang Q, Chen Y, et al.A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.Nucleic Acids Res 2024; 52(21):13447-13468.

[94]	Zhu T, Xia C, Yu R, Zhou X, Xu X, Wang L, et al.Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits.Nat Commun 2024; 15(1):6562.

[95]	Wang Z, Peng Y, Li J, Li J, Yuan H, Yang S, et al.DeepCBA: a deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions.Plant Commun 2024; 5(9):100985.

[96]	Daud KM, Mohamad MS, Zakaria Z, Hassan R, Shah ZA, Deris S, et al.A non-dominated sorting differential search algorithm flux balance analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout.Comput Biol Med 2019; 113:103390.

[97]	Sohn YJ, Hwang SY, Lee H, Jeon S, Park JY, Kim J, et al.Metabolic engineering of Corynebacterium glutamicum for high-level production of 1,5-pentanediol, a C5 diol platform chemical.Adv Sci 2024; 12(13):e2412670.

[98]	Yu T, Cui H, Li JC, Luo Y, Jiang G, Zhao H.Enzyme function prediction using contrastive learning.Science 2023; 379(6639):1358-1363.

[99]	Watson JL, Juergens D, Bennett NR, Trippe BL, Yim J, Eisenach HE, et al.De novo design of protein structure and function with RFdiffusion.Nature 2023; 620(7976):1089-1100.

[100]

Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, et al.Accurate structure prediction of biomolecular interactions with AlphaFold 3.Nature 2024; 630(8016):493-500.

[101]

Frank C, Khoshouei A, Fu Lß, Schiwietz D, Putz D.Scalable protein design using optimization in a relaxed sequence space.Science 2024; 386(6720):439-445.

[102]

An L, Said M, Tran L, Majumder S, Goreshnik I, Lee GR, et al.Binding and sensing diverse small molecules using shape-complementary pseudocycles.Science 2024; 385(6706):276-282.

[103]

Zhang Z, Wan XS, Liu Q, Zitnik M.Efficient generation of protein pockets with PocketGen.Nat Mach Intell 2024; 6(11):1382-1395.

[104]

Wang Y, Pang C, Wang Y, Jin J, Zhang J, Zeng X, et al.Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks.Nat Commun 2023; 14(1):6155.

[105]

Kim T, Lee S, Kwak Y, Choi MS, Park J, Hwang SJ, et al.READRetro: natural product biosynthesis predicting with retrieval-augmented dual-view retrosynthesis.New Phytol 2024; 243(6):2512-2527.

[106]

Zeng T, Jin Z, Zheng S, Yu T, Wu R.Developing BioNavi for hybrid retrosynthesis planning.JACS Au 2024; 4(7):2492-2502.

[107]

Wang Y, Wang S, Bai R, Li X, Yuan Y, Nan T, et al.Prediction performance and reliability evaluation of three ginsenosides in Panax ginseng using hyperspectral imaging combined with a novel ensemble chemometric model.Food Chem 2024; 430:136917.

[108]

Yue J, Li Z, Zuo Z, Wang Y.Evaluation of ecological suitability and quality suitability of Panax notoginseng under multi-regionalization modeling theory.Front Plant Sci 2022; 13:818376.

[109]

Shamshirgaran Y, Liu J, Sumer H, Verma PJ, Taheri-Ghahfarokhi A.Tools for efficient genome editing; ZFN, TALEN, and CRISPR.Methods Mol Biol 2022; 2495:29-46.

[110]

Kim YG, Cha J, Chandrasegaran S.Hybrid restriction enzymes: zinc finger fusions to Fok I cleavage domain.Proc Natl Acad Sci USA 1996; 93(3):1156-1160.

[111]

Urnov FD, Rebar EJ, Holmes MC, Zhang HS, Gregory PD.Genome editing with engineered zinc finger nucleases.Nat Rev Genet 2010; 11(9):636-646.

[112]

Boch J, Scholze H, Schornack S, Landgraf A, Hahn S, Kay S, et al.Breaking the code of DNA binding specificity of TAL-type III effectors.Science 2009; 326(5959):1509-1512.

[113]

Joung JK, Sander JD.TALENs: a widely applicable technology for targeted genome editing.Nat Rev Mol Cell Biol 2013; 14(1):49-55.

[114]

Garneau JE, MÈDupuis , Villion M, Romero DA, Barrangou R, Boyaval P, et al.The CRISPR/Cas bacterial immune system cleaves bacteriophage and plasmid DNA.Nature 2010; 468(7320):67-71.

[115]

Shi J, Wu Y, Zhang L.Decoding and designing: promising routes to tailor-made herbs.Sci Tradit Chin Med 2025; 3(2):194-197.

[116]

Komor AC, Kim YB, Packer MS, Zuris JA, Liu DR.Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage.Nature 2016; 533(7603):420-424.

[117]

Wang JY, Doudna JA.CRISPR technology: a decade of genome editing is only the beginning.Science 2023; 379(6629):eadd8643.

[118]

Peng Y, Zuo W, Zhou H, Miao F, Zhang Y, Qin Y, et al.EXPLICIT-Kinase: a gene expression predictor for dissecting the functions of the Arabidopsis kinome.J Integr Plant Biol 2022; 64(7):1374-1393.

[119]

Li X, Zheng H, Wu W, Liu H, Wang J, Jia Y, et al.QTL mapping and candidate gene analysis for Alkali tolerance in Japonica rice at the bud stage based on linkage mapping and genome-wide association study.Rice (N Y) 2020; 13(1):48.

[120]

Haussmann IU, Dix TC, McQuarrie DWJ, Dezi V, Hans AI, Arnold R, et al.Structure-optimized sgRNA selection with PlatinumCRISPr for efficient Cas9 generation of knockouts.Genome Res 2024; 34(12):2279-2292.

[121]

Kleinstiver BP, Pattanayak V, Prew MS, Tsai SQ, Nguyen NT, Zheng Z, et al.High-fidelity CRISPR-Cas9 nucleases with no detectable genome-wide off-target effects.Nature 2016; 529(7587):490-495.

[122]

Chen JS, Dagdas YS, Kleinstiver BP, Welch MM, Sousa AA, Harrington LB, et al.Enhanced proofreading governs CRISPR-Cas9 targeting accuracy.Nature 2017; 550(7676):407-410.

[123]

Wang H, Yan S, Wang W, Chen Y, Hong J, He Q, et al.Cropformer: an interpretable deep learning framework for crop genomic prediction.Plant Commun 2025; 6(3):101223.

[124]

Harfouche AL, Jacobson DA, Kainer D, Romero JC, Harfouche AH, Scarascia GMugnozza, et al.Accelerating climate resilient plant breeding by applying next-generation artificial intelligence.Trends Biotechnol 2019; 37(11):1217-1235.

[125]

Xu J, Huang DS, Zhang X.scmFormer integrates large-scale single-cell proteomics and transcriptomics data by multi-task transformer.Adv Sci 2024; 11(19):e2307835.

[126]

Shi J, Xie S, Li W, Wang X, Wang J, Chen Y, et al.RPT: an integrated root phenotyping toolbox for segmenting and quantifying root system architecture.Plant Biotechnol J 2025; 23(6):2095-2109.

[127]

Yu J, Zheng Y, Song C, Chen S.New insights into the roles of fungi and bacteria in the development of medicinal plant.J Adv Res 2024; 65:137-152.

[128]

Zhang S, Dai G, Huang T, Chen J.Multimodal large language models for bioimage analysis.Nat Methods 2024; 21(8):1390-1393.

[129]

Wu L, Han L, Li Q, Wang G, Zhang H, Li L.Using interactome big data to crack genetic mysteries and enhance future crop breeding.Mol Plant 2021; 14(1):77-94.

[130]

Yao B, Wang X, Wang Y, Ye T, Wang E, Cao Q, et al.Interaction of genotype, environment, and management on organ-specific critical nitrogen dilution curve in wheat.Plant Phenomics 2023; 5:0078.

[131]

Ma X, Wang H, Wu S, Han B, Cui D, Liu J, et al.DeepCCR: large-scale genomics-based deep learning method for improving rice breeding.Plant Biotechnol J 2024; 22(10):2691-2693.

[132]

Krishnamoorthy N, Narasimha LVPrasad, Pavan CSKumar, Subedi B, Abraha HB, Sathishkumar VE.Rice leaf diseases prediction using deep neural networks with transfer learning.Environ Res 2021; 198:111275.

[133]

Li W, Wang D, Li M, Gao Y, Wu J, Yang X.Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse.Comput Electron Agric 2021; 183:106048.

[134]

Qi C, Gao J, Pearson S, Harman H, Chen K, Shu L.Tea chrysanthemum detection under unstructured environments using the TC-YOLO model.Expert Syst Appl 2022; 193:116473.

[135]

Zhang X, Zhu D, Wen R.SwinT-YOLO: detection of densely distributed maize tassels in remote sensing images.Comput Electron Agric 2023; 210:107905.

[136]

Wang P, Meng F, Donaldson P, Horan S, Panchy NL, Vischulis E, et al.High-throughput measurement of plant fitness traits with an object detection method using Faster R-CNN.New Phytol 2022; 234(4):1521-1533.

[137]

Sari Rć, Nguyen VD, Burge T, Berkowitz O, Trtílek M, Whelan J, et al.Applications of hyperspectral imaging in plant phenotyping.Trends Plant Sci 2022; 27(3):301-315.

[138]

Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, et al.Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant Commun 2022; 3(6):100344.

[139]

An D, Zhang L, Liu Z, Liu J, Wei Y.Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality.Crit Rev Food Sci Nutr 2023; 63(29):9766-9796.

[140]

Pommier C, Michotey C, Cornut G, Roumet P, Duch Eêne, Flores R, et al.Applying FAIR principles to plant phenotypic data management in GnpIS.Plant Phenomics 2019; 2019:1671403.

[141]

De N Maio, Shaw LP, Hubbard A, George S, Sanderson ND, Swann J, et al.Comparison of long-read sequencing technologies in the hybrid assembly of complex bacterial genomes.Microb Genom 2019; 5(9):e000294.

[142]

Liu K, Abdullah AA, Huang M, Nishioka T, Altaf-Ul-Amin M, Kanaya S.Novel approach to classify plants based on metabolite-content similarity.BioMed Res Int 2017; 2017:5296729.

[143]

Widiyastuti Y, Subositi D.Photoperiod effect on the growth and artemisinin content of Artemisia annua grown in tropical region.AIP Conf Proc 2019; 2099(1):020027.

[144]

Lee HJ, Jeong J, Alves AC, Han ST, In G, Kim EH, et al.Metabolomic understanding of intrinsic physiology in Panax ginseng during whole growing seasons.J Ginseng Res 2019; 43(4):654-665.

[145]

Acosta-Pech R, Crossa J, de Glos Campos, Teyss Sèdre, Claustres B, P Sérez-Elizalde, et al.Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids.Theor Appl Genet 2017; 130(7):1431-1440.

[146]

Avraham S, Tung CW, Ilic K, Jaiswal P, Kellogg EA, McCouch S, et al.The Plant Ontology Database: a community resource for plant structure and developmental stages controlled vocabulary and annotations.Nucleic Acids Res 2008; 36(Database issue suppl_1):D449-D454.

[147]

Staton M, Cannon E, Sanderson LA, Wegrzyn J, Anderson T, Buehler S, et al.Tripal, a community update after 10 years of supporting open source, standards-based genetic, genomic and breeding databases.Brief Bioinform 2021; 22(6):bbab238.

[148]

Lamichhane JR, Alletto L.Ecosystem services of cover crops: a research roadmap.Trends Plant Sci 2022; 27(8):758-768.

[149]

Peake AS, Bell KL, Fischer RA, Gardner M, Das BT, Poole N, et al.Cultivar × management interaction to reduce lodging and improve grain yield of irrigated spring wheat: optimising plant growth regulator use, N application timing, row spacing and sowing date.Front Plant Sci 2020; 11:401.

[150]

Peng B, Guan K, Tang J, Ainsworth EA, Asseng S, Bernacchi CJ, et al.Towards a multiscale crop modelling framework for climate change adaptation assessment.Nat Plants 2020; 6(4):338-348.