MedMeta: An AI-Enabled and Genomics-Based Database for Functional Profiling of Secondary Metabolites in Medicinal Species

Engineering ›› DOI: 10.1016/j.eng.2025.09.007

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Abstract

Medicinal resources contain a vast array of secondary metabolites that play critical roles in disease treatment, health maintenance, and drug discovery. Nevertheless, challenges such as biosynthetic complexity and species-specific variability have long hindered their systematic exploration. Recent advances in omics technologies and artificial intelligence (AI)-driven approaches have opened new avenues via which to decode biosynthetic pathways and discover secondary metabolites using omics-level data. In this study, we present MedMeta, a curated and integrative database that connects secondary metabolites with genomic, biochemical, and pharmacological information across 1035 medicinal species documented in eight authoritative global pharmacopoeias. MedMeta comprises 146 101 predicted active secondary metabolites, 196 356 biosynthetic pathways, and an extensive set of annotated molecular targets. As a proof of principle, we employed MedMeta to investigate three representative Apiaceae species—Pucedanum praeruptorum, Angelica sinensis, and Apium graveolens—demonstrating its ability to uncover species-specific metabolite profiles, validate enzymatic functions, and identify compounds with important therapeutic potential. Overall, MedMeta can provide a powerful and scalable platform for natural product research, supporting both fundamental studies and applied biomedical applications. This database offers an invaluable resource for compound discovery, synthetic biology, geoherbalism studies, and the modern application of traditional medicinal systems.

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Secondary metabolites / Medicinal resources / Artificial intelligence / Drug discovery / Biosynthetic pathways

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Fanbo Meng, Guiyang Zhang, Wenke Xiao, Yufei Mao, Yun Shu, Xiuping Yang, Guoqing Xu, Xinyu Tang, Mengqing Zhang, Zhiyu Liu, Xunzhi Zhang, Shengjie You, Bin Wang, Zhiyin Yu, Shilin Chen, Wei Chen. MedMeta: An AI-Enabled and Genomics-Based Database for Functional Profiling of Secondary Metabolites in Medicinal Species. Engineering DOI:10.1016/j.eng.2025.09.007

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References

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

[1]	Ding Y, Brand E, Wang W, Zhao Z.Licorice: resources, applications in ancient and modern times.J Ethnopharmacol 2022; 298:115594.

[2]	Rizvi SAA, Einstein GP, Tulp OL, Sainvil F, Branly R.Introduction to traditional medicine and their role in prevention and treatment of emerging and re-emerging diseases.Biomolecules 2022; 12(10):1442.

[3]	Zhang X, Ji Z, He Q, Yang D, Wang X, Liu C, et al.Gegen Qinlian decoction inhibits liver ferroptosis in type 2 diabetes mellitus models by targeting Nrf2.J Ethnopharmacol 2025; 340:119290.

[4]	Chen J, Wei X, Zhang Q, Wu Y, Xia G, Xia H, et al.The traditional Chinese medicines treat chronic heart failure and their main bioactive constituents and mechanisms.Acta Pharm Sin B 2023; 13(5):1919-1955.

[5]	Han D, Chang X, Xu D, Shen J, Fan A, Wang M, et al.Yi-Qi-Huo-Xue decoction alleviates intracerebral hemorrhage injury through inhibiting neuronal autophagy of ipsilateral cortex via BDNF/TrkB pathway.Phytomedicine 2024; 128:155438.

[6]	World Health Organization.Integrating traditional medicine in health care.Geneva: World Health Organization; 2023 [cited 2025 May 25]. Available from: https://www.who.int/southeastasia/news/feature-stories/detail/integrating-traditional-medicine.

[7]	Liao B, Hu H, Xiao S, Zhou G, Sun W, Chu Y, et al.Global Pharmacopoeia Genome Database is an integrated and mineable genomic database for traditional medicines derived from eight international pharmacopoeias.Sci China Life Sci 2022; 65(4):809-817.

[8]	Li Y, Kong D, Fu Y, Sussman MR, Wu H.The effect of developmental and environmental factors on secondary metabolites in medicinal plants, plant physiology and biochemistry.Plant Physiol Biochem 2020; 148:80-89.

[9]	Harvey AL, Edrada-Ebel R, Quinn RJ.The re-emergence of natural products for drug discovery in the genomics era.Nat Rev Drug Discov 2015; 14(2):111-129.

[10]	White NJ.Qinghaosu (artemisinin): the price of success.Science 2008; 320(5874):330-334.

[11]	Wang T, Li L, Zhuang W, Zhang F, Shu X, Wang N, et al.Recent research progress in taxol biosynthetic pathway and acylation reactions mediated by taxus acyltransferases.Molecules 2021; 26(10):2855.

[12]	Chandrasekhar V, Rajan K, Kanakam SRS, 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.

[13]	Gallo K, Kemmler E, Goede A, Becker F, Dunkel M, Preissner R, et al.SuperNatural 3.0—a database of natural products and natural product-based derivatives.Nucleic Acids Res 2023; 51(D1):D654-D659.

[14]	Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, et al.Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.Nat Biotechnol 2016; 34(8):828-837.

[15]	Bi Z, Li H, Liang Y, Sun D, Liu S, Chen W, et al.Emerging paradigms for target discovery of traditional medicines: a genome-wide pan-GPCR perspective.Innovation 2025; 6(3):100774.

[16]	Zhang T, An W, You S, Chen S, Zhang S.G protein-coupled receptors and traditional chinese medicine: new thinks for the development of traditional Chinese medicine.Chin Med 2024; 19(1):92.

[17]	Atanasov AG, Zotchev SB, Dirsch VM, Supuran CT.Natural products in drug discovery: advances and opportunities.Nat Rev Drug Discov 2021; 20(3):200-216.

[18]	Lauti Eé, Russo O, Ducrot P, Boutin JA.Unraveling plant natural chemical diversity for drug discovery purposes.Front Pharmacol 2020; 11:397.

[19]	Chen W, Song C, Leng L, Zhang S, Chen S.The application of artificial intelligence accelerates G protein-coupled receptor ligand discovery.Engineering 2024; 32:18-28.

[20]	Mullowney MW, Duncan KR, Elsayed SS, Garg N, van JJJ der Hooft, Martin NI, et al.Artificial intelligence for natural product drug discovery.Nat Rev Drug Discov 2023; 22(11):895-916.

[21]	Sahayasheela VJ, Lankadasari MB, Dan VM, Dastager SG, Pandian GN, Sugiyama H.Artificial intelligence in microbial natural product drug discovery: current and emerging role.Nat Prod Rep 2022; 39(12):2215-2230.

[22]	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.

[23]	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.

[24]	Tong X, Qu N, Kong X, Ni S, Zhou J, Wang K, et al.Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.Nat Commun 2024; 15(1):5378.

[25]	Qi X, Zhao L, Tian C, Li Y, Chen ZL, Huo P, et al.Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery.Nat Commun 2024; 15(1):9256.

[26]	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; 101011:101011.

[27]	Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al.PubChem in 2021: new data content and improved web interfaces.Nucleic Acids Res 2021; 49(D1):D1388-D1395.

[28]	Kim HW, Wang M, Leber CA, Nothias LF, Reher R, Kang KB, et al.NPClassifier: a deep neural network-based structural classification tool for natural products.J Nat Prod 2021; 84(11):2795-2807.

[29]	Swanson K, Walther P, Leitz J, Mukherjee S, Wu JC, Shivnaraine RV, et al.ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries.Bioinformatics 2024; 40(7):btae416.

[30]	Yamanishi Y, Hattori M, Kotera M, Goto S, Kanehisa M.E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs.Bioinformatics 2009; 25(12):i179-i186.

[31]	Knox C, Wilson M, Klinger CM, Franklin M, Oler E, Wilson A, et al.DrugBank 6.0: the DrugBank Knowledgebase for 2024.Nucleic Acids Res 2024; 52(D1):D1265-D1275.

[32]	Gallo K, Goede A, Preissner R, Gohlke BO.SuperPred 3.0: drug classification and target prediction-a machine learning approach.Nucleic Acids Res 2022; 50(W1):W726-W731.

[33]	Fang Z, Liu X, Peltz G.GSEApy: a comprehensive package for performing gene set enrichment analysis in Python.Bioinformatics 2023; 39(1):btac757.

[34]	Yu G, Wang LG, Han Y, He QY.clusterProfiler: an R package for comparing biological themes among gene clusters.OMICS 2012; 16(5):284-287.

[35]	Meng F, Chu T, Feng P, Li N, Song C, Li C, et al.Genome assembly of Polygala tenuifolia provides insights into its karyotype evolution and triterpenoid saponin biosynthesis.Hortic Res 2023; 10(9):uhad139.

[36]	Huang XC, Tang H, Wei X, He Y, Hu S, Wu JY, et al.The gradual establishment of complex coumarin biosynthetic pathway in Apiaceae.Nat Commun 2024; 15(1):6864.

[37]	Song Y, Zhang Y, Wang X, Yu X, Liao Y, Zhang H, et al.Telomere-to-telomere reference genome for Panax ginseng highlights the evolution of saponin biosynthesis.Hortic Res 2024; 11(6):uhae107.

[38]	Jiang Z, Tu L, Yang W, Zhang Y, Hu T, Ma B, et al.The chromosome-level reference genome assembly for Panax notoginseng and insights into ginsenoside biosynthesis.Plant Commun 2021; 2(1):100113.

[39]	Yang X, Meng F, Cheng Q, Feng P, Song X, Chen W.Evolution of terpene synthases in the sesquiterpene biosynthesis pathway and analysis of their transcriptional regulatory network in Asteraceae. Hortic Res (2025), Article uhaf229

[40]	Kautsar SA, Suarez HG Duran, Blin K, Osbourn A, Medema MH.plantiSMASH: automated identification, annotation and expression analysis of plant biosynthetic gene clusters.Nucleic Acids Res 2017; 45(W1):W55-W63.

[41]	Liu SJ, Liu Z, Shao BY, Li T, Zhu X, Wang R, et al.Deciphering the biosynthetic pathway of triterpene saponins in Prunella vulgaris.Plant J 2025; 121(2):e17220.

[42]	Seo YJ, Kwon MS, Park SH, Sim YB, Choi SM, Huh GH, et al.The analgesic effect of decursinol.Arch Pharm Res 2009; 32(6):937-943.

[43]	Son SH, Park KK, Park SK, Kim YC, Kim YS, Lee SK, et al.Decursin and decursinol from Angelica gigas inhibit the lung metastasis of murine colon carcinoma.Phytother Res 2011; 25(7):959-964.

[44]	Sestito S, Ibba R, Riu F, Carpi S, Carta A, Manera C, et al.Anticancer potential of decursin, decursinol angelate, and decursinol from Angelica gigas Nakai: a comprehensive review and future therapeutic prospects.Food Sci Nutr 2024; 12(10):6970-6989.

[45]	Crawford L, Henderson-Redmond A, Kim S, Karelia D, Sepulveda D, Lu J, et al.Assessing the antinociceptive effects and tolerance development of decursinol in nociceptive, inflammatory, and neuropathic pain.FASEB J 2022; 36(S1):fasebj.

[46]	Han T, Miao G.Strategies, achievements, and potential challenges of plant and microbial chassis in the biosynthesis of plant secondary metabolites.Molecules 2024; 29(9):2106.