{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T10:16:40Z","timestamp":1788257800588,"version":"build-2803163510"},"reference-count":81,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82505197"],"award-info":[{"award-number":["82505197"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["82505197"],"award-info":[{"award-number":["82505197"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&D Program of Zhejiang","award":["2025C01110"],"award-info":[{"award-number":["2025C01110"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["BX20240318"],"award-info":[{"award-number":["BX20240318"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"award":["BX20240318"],"award-info":[{"award-number":["BX20240318"]}],"id":[{"id":"https:\/\/ror.org\/0426zh255","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Hangzhou Key Scientific Research Plan Projects","award":["2025SZD1B25"],"award-info":[{"award-number":["2025SZD1B25"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>High-content screening (HCS) is a useful phenotypic drug discovery technology that combines automated microscopy, image analysis, and high-throughput experimentation to comprehensively characterize biological responses to diverse perturbations. This review summarizes the methodological fundamentals of high-content analysis, including image preprocessing, cell segmentation, feature processing, and downstream analysis, as well as the diverse phenotypic datasets generated from different biological models, perturbation strategies, and staining approaches. Recent advances in artificial intelligence, particularly deep learning, have improved cell segmentation, image representation learning, and phenotypic profiling, enabling more accurate and scalable analysis of HCS data. We further highlight emerging applications of AI-powered HCS in pharmaceutical research, with a particular focus on the discovery of bioactive compounds from natural sources. Finally, we discuss current challenges and future perspectives, including the construction of large-scale phenotypic databases, the integration of AI throughout the screening workflow, and the development of intelligent screening platforms. These advances are expected to accelerate phenotype-driven drug discovery and promote innovation in natural product research.<\/jats:p>","DOI":"10.3390\/molecules31173075","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T09:48:36Z","timestamp":1788256116000},"page":"3075","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Powered High-Content Analysis: Methodologies and Applications in Bioactive Compound Discovery from Natural Sources"],"prefix":"10.3390","volume":"31","author":[{"given":"Dejin","family":"Xun","sequence":"first","affiliation":[{"name":"Pharmaceutical Informatics Institute, School of Pharmacy, Zhejiang University, Hangzhou 310058, China"},{"name":"National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Pharmaceutical Informatics Institute, School of Pharmacy, Zhejiang University, Hangzhou 310058, China"},{"name":"Department of Dermatology, Hangzhou Third People\u2019s Hospital, 38 Xihu Avenue, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, 333 Longteng Road, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0763-1513","authenticated-orcid":false,"given":"Yingchao","family":"Wang","sequence":"additional","affiliation":[{"name":"Pharmaceutical Informatics Institute, School of Pharmacy, Zhejiang University, Hangzhou 310058, China"},{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6336-3007","authenticated-orcid":false,"given":"Xiaohui","family":"Fan","sequence":"additional","affiliation":[{"name":"Pharmaceutical Informatics Institute, School of Pharmacy, Zhejiang University, Hangzhou 310058, China"},{"name":"National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3676-9183","authenticated-orcid":false,"given":"Yi","family":"Wang","sequence":"additional","affiliation":[{"name":"Pharmaceutical Informatics Institute, School of Pharmacy, Zhejiang University, Hangzhou 310058, China"},{"name":"National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China"},{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.chembiol.2021.02.015","article-title":"High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need","volume":"28","author":"Hughes","year":"2021","journal-title":"Cell Chem. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.tibtech.2010.02.005","article-title":"High content screening: Seeing is believing","volume":"28","author":"Zanella","year":"2010","journal-title":"Trends Biotechnol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1177\/1087057114528537","article-title":"Increasing the content of high-content screening: An overview","volume":"19","author":"Singh","year":"2014","journal-title":"SLAS Discov."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1016\/j.cell.2015.11.007","article-title":"Microscopy-based high-content screening","volume":"163","author":"Boutros","year":"2015","journal-title":"Cell"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1016\/j.drudis.2020.06.001","article-title":"Image-based high-content screening in drug discovery","volume":"25","author":"Lin","year":"2020","journal-title":"Drug Discov. Today"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1038\/s41573-020-00117-w","article-title":"Image-based profiling for drug discovery: Due for a machine-learning upgrade?","volume":"20","author":"Chandrasekaran","year":"2020","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cbpa.2021.04.001","article-title":"Image-based cell phenotyping with deep learning","volume":"65","author":"Pratapa","year":"2021","journal-title":"Curr. Opin. Chem. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.tcb.2022.11.011","article-title":"Deep learning in image-based phenotypic drug discovery","volume":"33","author":"Krentzel","year":"2023","journal-title":"Trends Cell Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.tcb.2016.03.008","article-title":"High-content screening for quantitative cell biology","volume":"26","author":"Styles","year":"2016","journal-title":"Trends Cell Biol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1517\/17460441.2012.711311","article-title":"Functional genomic and high-content screening for target discovery and deconvolution","volume":"7","author":"Pache","year":"2012","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3387","DOI":"10.1007\/s00204-019-02593-5","article-title":"Review of high-content screening applications in toxicology","volume":"93","author":"Li","year":"2019","journal-title":"Arch. Toxicol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s10545-017-0055-1","article-title":"High-content drug screening for rare diseases","volume":"40","author":"Bellomo","year":"2017","journal-title":"J. Inherit. Metab. Dis."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1111\/pcn.13847","article-title":"Linking autism risk genes to morphological and pharmaceutical screening by high-content imaging: Future directions and opinion","volume":"79","author":"Arta","year":"2025","journal-title":"Psychiatry Clin. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1038\/s42003-025-08758-6","article-title":"An effective system for senescence modulating drug development using quantitative high-content analysis and high-throughput screening","volume":"8","author":"Hu","year":"2025","journal-title":"Commun. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s00449-024-03110-4","article-title":"High-content imaging and deep learning-driven detection of infectious bacteria in wounds","volume":"48","author":"Zhang","year":"2025","journal-title":"Bioprocess Biosyst. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/nrd1657","article-title":"The evolving role of natural products in drug discovery","volume":"4","author":"Koehn","year":"2005","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.drudis.2008.07.004","article-title":"Natural products in drug discovery","volume":"13","author":"Harvey","year":"2008","journal-title":"Drug Discov. Today"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1111\/1751-7915.13351","article-title":"Unlocking the potential of natural products in drug discovery","volume":"12","author":"Wright","year":"2019","journal-title":"Microb. Biotechnol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1038\/s41573-020-00114-z","article-title":"Natural products in drug discovery: Advances and opportunities","volume":"20","author":"Atanasov","year":"2021","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"nwac206","DOI":"10.1093\/nsr\/nwac206","article-title":"Natural products and drug discovery","volume":"9","author":"Newman","year":"2022","journal-title":"Natl. Sci. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_22","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical vision transformer using shifted windows. Proceedings of the International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_24","unstructured":"He, K., and Chen, X. (2021, January 19\u201325). Masked autoencoders are scalable vision learners. Proceedings of the Computer Vision and Pattern Recognition, Nashville, Tennessee."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","article-title":"Cellpose: A generalist algorithm for cellular segmentation","volume":"18","author":"Stringer","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1038\/nrg2825","article-title":"Tackling the widespread and critical impact of batch effects in high-throughput data","volume":"11","author":"Leek","year":"2010","journal-title":"Nat. Rev. Genet."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, A., and Lu, A.X. (2022). Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images. Proceedings of Machine Learning Research, JMLR.","DOI":"10.1101\/2022.10.14.512286"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1097\/HM9.0000000000000105","article-title":"Guidelines for application of high-content screening in traditional Chinese medicine: Concept, equipment, and troubleshooting","volume":"4","author":"Chen","year":"2024","journal-title":"Acupunct. Herb. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/nmeth.3323","article-title":"CIDRE: An illumination-correction method for optical microscopy","volume":"12","author":"Smith","year":"2015","journal-title":"Nat. Methods"},{"key":"ref_30","unstructured":"Yi, F., and Moon, I. (2012, January 19\u201321). Image segmentation: A survey of graph-cut methods. Proceedings of the International Conference on Systems and Informatics, Yantai, China."},{"key":"ref_31","unstructured":"Sommer, C., Straehle, C., Kothe, U., and Hamprecht, F.A. (April, January 30). Ilastik: Interactive learning and segmentation toolkit. Proceedings of the IEEE International Symposium on Biomedical Imaging, Chicago, IL, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"R100","DOI":"10.1186\/gb-2006-7-10-r100","article-title":"CellProfiler: Image analysis software for identifying and quantifying cell phenotypes","volume":"7","author":"Carpenter","year":"2006","journal-title":"Genome. Biol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"14267","DOI":"10.1021\/acs.analchem.0c03741","article-title":"Deep-learning-assisted assessment of DNA damage based on Foci images and its application in high-content screening of lead compounds","volume":"92","author":"Chen","year":"2020","journal-title":"Anal. Chem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1038\/s42003-025-09500-y","article-title":"Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships","volume":"9","author":"Sanchez","year":"2026","journal-title":"Commun. Biol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e24060","DOI":"10.7554\/eLife.24060","article-title":"Systematic morphological profiling of human gene and allele function via Cell Painting","volume":"6","author":"Rohban","year":"2017","journal-title":"eLife"},{"key":"ref_36","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/nbt.4314","article-title":"Dimensionality reduction for visualizing single-cell data using UMAP","volume":"37","author":"Becht","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.coph.2010.05.001","article-title":"Efficacy of anti-cancer agents in cell lines versus human primary tumour tissue","volume":"10","author":"Cree","year":"2010","journal-title":"Curr. Opin. Pharmacol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1161\/CIRCRESAHA.108.176131","article-title":"Forced alignment of mesenchymal stem cells undergoing cardiomyogenic differentiation affects functional integration with cardiomyocyte cultures","volume":"103","author":"Pijnappels","year":"2008","journal-title":"Circ. Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kuhn, M.R., Wolcott, E.A., and Langer, E.M. (2025). Developments in gastrointestinal organoid cultures to recapitulate tissue environments. Front. Bioeng. Biotechnol., 13.","DOI":"10.3389\/fbioe.2025.1521044"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1186\/s10020-025-01131-7","article-title":"Effects of hydrogel stiffness and viscoelasticity on organoid culture: A comprehensive review","volume":"31","author":"Lai","year":"2025","journal-title":"Mol. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1038\/s41587-024-02344-7","article-title":"Organoid culture promotes dedifferentiation of mouse myoblasts into stem cells capable of complete muscle regeneration","volume":"43","author":"Price","year":"2025","journal-title":"Nat. Biotechnol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2407572","DOI":"10.1002\/advs.202407572","article-title":"Chemogenomic screening in a patient-derived 3D fatty liver disease model reveals the CHRM1-TRPM8 axis as a novel module for targeted intervention","volume":"12","author":"Youhanna","year":"2025","journal-title":"Adv. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Torres, A.K., Mira, R.G., Pinto, C., and Inestrosa, N.C. (2025). Studying the mechanisms of neurodegeneration: C. elegans advantages and opportunities. Front. Cell. Neurosci., 19.","DOI":"10.3389\/fncel.2025.1559151"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rani, N., Alam, M.M., and Parvez, S. (2025). Toxicity of glyphosate accelerates neurodegeneration in Caenorhabditis elegans model of Alzheimer\u2019s disease. Front. Toxicol., 7.","DOI":"10.3389\/ftox.2025.1578230"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e70007","DOI":"10.1111\/acel.70007","article-title":"Development of a short telomere zebrafish model for accelerated aging research and antiaging drug screening","volume":"24","author":"Cayuela","year":"2025","journal-title":"Aging Cell"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e70080","DOI":"10.1002\/bies.70080","article-title":"Zebrafish: A versatile and powerful model for biomedical research","volume":"47","author":"Siddiqui","year":"2025","journal-title":"BioEssays"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"101346","DOI":"10.1016\/j.cels.2025.101346","article-title":"Integrated time-series analysis and high-content CRISPR screening delineate the dynamics of macrophage immune regulation","volume":"16","author":"Traxler","year":"2025","journal-title":"Cell Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Schmidt, U., Weigert, M., Broaddus, C., and Myers, G. (2018, January 16\u201320). Cell detection with star-convex polygons. Proceedings of the Medical Image Computing and Computer Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00934-2_30"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"101563","DOI":"10.1016\/j.media.2019.101563","article-title":"Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images","volume":"58","author":"Graham","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"105506","DOI":"10.1016\/j.isci.2022.105506","article-title":"Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning","volume":"25","author":"Xun","year":"2022","journal-title":"iScience"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1038\/s42003-025-08190-w","article-title":"A self-supervised learning approach for high throughput and high content cell segmentation","volume":"8","author":"Lam","year":"2025","journal-title":"Commun. Biol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1038\/s41592-021-01249-6","article-title":"LIVECell-a large-scale dataset for label-free live cell segmentation","volume":"18","author":"Edlund","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41587-021-01094-0","article-title":"Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning","volume":"40","author":"Greenwald","year":"2022","journal-title":"Nat. Biotechnol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1038\/s41592-022-01663-4","article-title":"Cellpose 2.0: How to train your own model","volume":"19","author":"Pachitariu","year":"2022","journal-title":"Nat. Methods"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1038\/s41592-025-02595-5","article-title":"Cellpose3: One-click image restoration for improved cellular segmentation","volume":"22","author":"Stringer","year":"2025","journal-title":"Nat. Methods"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Pawlowski, N., Caicedo, J.C., Singh, S., Carpenter, A.E., and Storkey, A. (2016). Automating morphological profiling with generic deep convolutional networks. bioRxiv, 085118.","DOI":"10.1101\/085118"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Perakis, A., Gorji, A., Jain, S., Chaitanya, K., Rizza, S., and Konukoglu, E. (2021, January 27). Contrastive learning of single-cell phenotypic representations for treatment classification. Proceedings of the Machine Learning in Medical Imaging, Strasbourg, France.","DOI":"10.1007\/978-3-030-87589-3_58"},{"key":"ref_59","unstructured":"Hua, S.B.Z., Lu, A.X., and Moses, A.M. (2021, January 6\u201314). CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning. Proceedings of the Neural Information Processing Systems, Online."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1038\/s41592-022-01541-z","article-title":"Self-supervised deep learning encodes high-resolution features of protein subcellular localization","volume":"19","author":"Kobayashi","year":"2022","journal-title":"Nat. Methods"},{"key":"ref_61","first-page":"100541","article-title":"Microsnoop: A generalist tool for microscopy image representation","volume":"5","author":"Xun","year":"2024","journal-title":"Innovation"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"e68714","DOI":"10.7554\/eLife.68714","article-title":"Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes","volume":"10","author":"Grafton","year":"2021","journal-title":"eLife"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2400635","DOI":"10.1002\/aisy.202400635","article-title":"\u03a0-PhenoDrug: A comprehensive deep learning-based pipeline for phenotypic drug screening in high-content analysis","volume":"7","author":"Li","year":"2025","journal-title":"Adv. Intell. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1038\/s41467-022-28423-4","article-title":"Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts","volume":"13","author":"Schiff","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e10238","DOI":"10.1016\/j.heliyon.2022.e10238","article-title":"High-content screening of active components of traditional Chinese medicine inhibiting TGF-\u03b2-induced cell EMT","volume":"8","author":"Xu","year":"2022","journal-title":"Heliyon"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Li, J., Liu, H., Yang, Z., Yu, Q., Zhao, L., and Wang, Y. (2021). Synergistic effects of cryptotanshinone and senkyunolide I in guanxinning tablet against endogenous thrombus formation in zebrafish. Front. Pharmacol., 11.","DOI":"10.3389\/fphar.2020.622787"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"114667","DOI":"10.1016\/j.jep.2021.114667","article-title":"Salvianolic acid B and ferulic acid synergistically promote angiogenesis in HUVECs and zebrafish via regulating VEGF signaling","volume":"283","author":"Chen","year":"2022","journal-title":"J. Ethnopharmacol."},{"key":"ref_68","first-page":"379586","article-title":"Safety evaluation of Chinese medicine injections with a cell imaging-based multiparametric assay revealed a critical involvement of mitochondrial function in hepatotoxicity","volume":"2015","author":"Wang","year":"2015","journal-title":"J. Evid.-Based Complement. Altern. Med."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1016\/j.jare.2025.02.028","article-title":"Deep-DPC: Deep learning-assisted label-free temporal imaging discovery of anti-fibrotic compounds by controlling cell morphology","volume":"78","author":"Xing","year":"2025","journal-title":"J. Adv. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s12931-025-03173-1","article-title":"CPHNet: A novel pipeline for anti-HAPE drug screening via deep learning-based cell painting scoring","volume":"26","author":"Sun","year":"2025","journal-title":"Respir. Res."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Fang, J., Wu, X., Meng, X., Xun, D., Xu, S., and Wang, Y. (2022). Discovery of natural small molecules promoting collagen secretion by high-throughput screening in Caenorhabditis elegans. Molecules, 27.","DOI":"10.3390\/molecules27238361"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1038\/s41592-024-02528-8","article-title":"Cell Painting: A decade of discovery and innovation in cellular imaging","volume":"22","author":"Seal","year":"2024","journal-title":"Nat. Methods"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1080\/17460441.2020.1767063","article-title":"An up-to-date overview of computational polypharmacology in modern drug discovery","volume":"15","author":"Chaudhari","year":"2020","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/17460441.2024.2376643","article-title":"Polypharmacology prediction: The long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery","volume":"19","author":"Antolin","year":"2024","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_75","unstructured":"Liu, Z., and He, K. (2025, January 24\u201328). A decade\u2019s battle on dataset bias: Are we there yet?. Proceedings of the International Conference on Learning Representations, Singapore."},{"key":"ref_76","first-page":"1","article-title":"Artificial intelligence breakthroughs and data futures: A retrospective and prospective review","volume":"14","year":"2026","journal-title":"Acad. Platf. J. Eng. Smart Syst."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1038\/s41592-025-02762-8","article-title":"DSI studio: An integrated tractography platform and fiber data hub for accelerating brain research","volume":"22","author":"Yeh","year":"2025","journal-title":"Nat. Methods"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1039\/D4NP00008K","article-title":"Empowering natural product science with AI: Leveraging multimodal data and knowledge graphs","volume":"42","author":"Meijer","year":"2025","journal-title":"Nat. Prod. Rep."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s44159-024-00392-z","article-title":"Using natural language processing to analyse text data in behavioural science","volume":"4","author":"Feuerriegel","year":"2025","journal-title":"Nat. Rev. Psychol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s12525-025-00862-z","article-title":"Multi-agent AI","volume":"36","author":"Allmendinger","year":"2026","journal-title":"Electron. Mark."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1038\/s41586-026-10652-y","article-title":"A multi-agent system for automating scientific discovery","volume":"655","author":"Ghareeb","year":"2026","journal-title":"Nature"}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/31\/17\/3075\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T09:49:18Z","timestamp":1788256158000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/31\/17\/3075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":81,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["molecules31173075"],"URL":"https:\/\/doi.org\/10.3390\/molecules31173075","relation":{},"ISSN":["1420-3049"],"issn-type":[{"value":"1420-3049","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,1]]}}}