[1]

Zhu Y, Ouyang Z, Du H, Wang M, Wang J, et al. 2022. New opportunities and challenges of natural products research: when target identification meets single-cell multiomics. Acta Pharmaceutica Sinica B 12:4011−4039

doi: 10.1016/j.apsb.2022.08.022
[2]

Li G, Shi Q, Wu Q, Sui X. 2025. Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches. Cancer Biology & Medicine 22:549−597

doi: 10.20892/j.issn.2095-3941.2025.0145
[3]

Liu TT, Zeng KW. 2025. Recent advances in target identification technology of natural products. Pharmacology & Therapeutics 269:108833

doi: 10.1016/j.pharmthera.2025.108833
[4]

Bordukova M, Makarov N, Rodriguez-Esteban R, Schmich F, Menden MP. 2024. Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. Expert Opinion on Drug Discovery 19:33−42

doi: 10.1080/17460441.2023.2273839
[5]

You Y, Lai X, Pan Y, Zheng H, Vera J, et al. 2022. Artificial intelligence in cancer target identification and drug discovery. Signal Transduction and Targeted Therapy 7:156

doi: 10.1038/s41392-022-00994-0
[6]

Rozera T, Pasolli E, Segata N, Ianiro G. 2025. Machine learning and artificial intelligence in the multi-omics approach to gut microbiota. Gastroenterology 169:487−501

doi: 10.1053/j.gastro.2025.02.035
[7]

Lee S, Kim J, Jung HU, Kim D, Cho E, et al. 2026. Artificial intelligence and multiomics integration for Parkinson's disease drug development. Molecules and Cells 49:100343

doi: 10.1016/j.mocell.2026.100343
[8]

Corrales F, Cardinale V. 2026. Multiomics- and artificial intelligence-powered research platforms for enhancing understanding and prediction of the cholangiocarcinoma patient journey. Gut 75:1272−1274

doi: 10.1136/gutjnl-2025-337219
[9]

Liu Y, Jiang JJ, Du SY, Mu LS, Fan JJ, et al. 2024. Artemisinins ameliorate polycystic ovarian syndrome by mediating LONP1-CYP11A1 interaction. Science 384:eadk5382

doi: 10.1126/science.adk5382
[10]

Wang Y, Zhang Y, Luo H, Wei W, Liu W, et al. 2024. Identification of USP2 as a novel target to induce degradation of KRAS in myeloma cells. Acta Pharmaceutica Sinica B 14:5235−5248

doi: 10.1016/j.apsb.2024.08.019
[11]

Hueber W, Kidd BA, Tomooka BH, Lee BJ, Bruce B, et al. 2005. Antigen microarray profiling of autoantibodies in rheumatoid arthritis. Arthritis & Rheumatism 52:2645−2655

doi: 10.1002/art.21269
[12]

Huang J, Zhu H, Haggarty SJ, Spring DR, Hwang H, et al. 2004. Finding new components of the target of rapamycin (TOR) signaling network through chemical genetics and proteome chips. Proceedings of the National Academy of Sciences of the United States of America 101:16594−16599

doi: 10.1073/pnas.0407117101
[13]

Zhao MM, Ren TT, Wang JK, Yao L, Liu TT, et al. 2025. Endoplasmic reticulum membrane remodeling by targeting reticulon-4 induces pyroptosis to facilitate antitumor immune. Protein & Cell 16:121−135

doi: 10.1093/procel/pwae049
[14]

Zhang XW, Feng N, Liu YC, Guo Q, Wang JK, et al. 2022. Neuroinflammation inhibition by small-molecule targeting USP7 noncatalytic domain for neurodegenerative disease therapy. Science Advances 8:eabo0789

doi: 10.1126/sciadv.abo0789
[15]

Chen X, Wang Y, Ma N, Tian J, Shao Y, et al. 2020. Target identification of natural medicine with chemical proteomics approach: probe synthesis, target fishing and protein identification. Signal Transduction and Targeted Therapy 5:72

doi: 10.1038/s41392-020-0186-y
[16]

Siriwongsup S, Schmoker AM, Ficarro SB, Marto JA, Kim J. 2024. Bioorthogonally activated reactive species for target identification. Chem 10:1306−1315

doi: 10.1016/j.chempr.2024.03.002
[17]

Wang X, Liew SS, Huang J, Hu Y, Wei X, et al. 2024. Dual-locked enzyme-activatable bioorthogonal fluorescence turn-on imaging of senescent cancer cells. Journal of the American Chemical Society 146:22689−22698

doi: 10.1021/jacs.4c07286
[18]

Wang Q, Du T, Zhang Z, Zhang Q, Zhang J, et al. 2024. Target fishing and mechanistic insights of the natural anticancer drug candidate chlorogenic acid. Acta Pharmaceutica Sinica B 14:4431−4442

doi: 10.1016/j.apsb.2024.07.005
[19]

Gao P, Wang J, Qiu C, Zhang H, Wang C, et al. 2024. Photoaffinity probe-based antimalarial target identification of artemisinin in the intraerythrocytic developmental cycle of Plasmodium falciparum. iMeta 3:e176

doi: 10.1002/imt2.176
[20]

Martín-Acosta P, Meng Q, Klimek J, Reddy AP, David L, et al. 2022. A clickable photoaffinity probe of betulinic acid identifies tropomyosin as a target. Acta Pharmaceutica Sinica B 12:2406−2416

doi: 10.1016/j.apsb.2021.12.008
[21]

Li F, Cai C, Wang F, Zhang N, Zhao Q, et al. 2025. 20(S)-ginsenoside Rg3 suppresses gastric cancer cell proliferation by inhibiting E2F-DP dimerization. Phytomedicine 141:156740

doi: 10.1016/j.phymed.2025.156740
[22]

Peng W, Shi D, Xu D, Wang X, Cai Y, et al. 2026. Identification of Bruceine A as a novel HSP90AB1 inhibitor for suppressing hepatocellular carcinoma growth. Journal of Advanced Research 82:863−879

doi: 10.1016/j.jare.2025.07.016
[23]

Lin C, Wan Y, Huo Q, Liu D, Liu X, et al. 2025. Chemoproteomics reveals ailanthone directly binds to PKM2 to inhibit the progression of hepatocellular carcinoma. Phytomedicine 143:156886

doi: 10.1016/j.phymed.2025.156886
[24]

Wu Y, Li Y, Huang Y, Li Q, Li Z, et al. 2025. Nobiletin promotes ferroptosis in breast cancer through targeting AKR1C1-mediated ubiquitination and degradation of GPX4. Phytomedicine 146:157074

doi: 10.1016/j.phymed.2025.157074
[25]

Wu Y, Yang Y, Wang W, Sun D, Liang J, et al. 2022. PROTAC technology as a novel tool to identify the target of lathyrane diterpenoids. Acta pharmaceutica Sinica B 12:4262−4265

doi: 10.1016/j.apsb.2022.07.007
[26]

Ni Z, Shi Y, Liu Q, Wang L, Sun X, et al. 2024. Degradation-based protein profiling: a case study of celastrol. Advanced Science 11:2308186

doi: 10.1002/advs.202308186
[27]

Lomenick B, Hao R, Jonai N. 2010. Target identification using drug affinity responsive target stability (DARTS). Science-Business eXchange 3:71

doi: 10.1038/scibx.2010.71
[28]

Lomenick B, Jung G, Wohlschlegel JA, Huang J. 2011. Target identification using drug affinity responsive target stability (DARTS). Current Protocols in Chemical Biology 3:163−180

doi: 10.1002/9780470559277.ch110180
[29]

Guo W, Zhou H, Wang J, Lu J, Dong Y, et al. 2024. Aloperine suppresses cancer progression by interacting with VPS4A to inhibit autophagosome-lysosome fusion in NSCLC. Advanced Science 11:e2308307

doi: 10.1002/advs.202308307
[30]

Hu J, Liu W, Zou Y, Jiao C, Zhu J, et al. 2024. Allosterically activating SHP2 by oleanolic acid inhibits STAT3–Th17 axis for ameliorating colitis. Acta Pharmaceutica Sinica B 14:2598−2612

doi: 10.1016/j.apsb.2024.03.017
[31]

Martinez Molina D, Jafari R, Ignatushchenko M, Seki T, Larsson EA, et al. 2013. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341:84−87

doi: 10.1126/science.1233606
[32]

Miettinen TP, Björklund M. 2014. NQO2 is a reactive oxygen species generating off-target for acetaminophen. Molecular Pharmaceutics 11:4395−4404

doi: 10.1021/mp5004866
[33]

Ji H, Lu X, Zhao S, Wang Q, Liao B, et al. 2023. Target deconvolution with matrix-augmented pooling strategy reveals cell-specific drug-protein interactions. Cell Chemical Biology 30:1478−1487.e7

doi: 10.1016/j.chembiol.2023.08.002
[34]

Liu R, Zhang Y, Zou H, Zhang M, Yang Z, et al. 2026. Ginkgolic acid targets HSPA8 to trigger ferroptosis in hepatocellular carcinoma via chaperone-mediated autophagy-dependent GPX4 degradation. Pharmaceutical Biology 64:514−535

doi: 10.1080/13880209.2026.2646350
[35]

Yang A, Zeng K, Huang H, Liu D, Song X, et al. 2023. Usenamine A induces apoptosis and autophagic cell death of human hepatoma cells via interference with the Myosin-9/actin-dependent cytoskeleton remodeling. Phytomedicine 116:154895

doi: 10.1016/j.phymed.2023.154895
[36]

Li Y, Dong M, Qin H, An G, Cen L, et al. 2025. Mulberrin suppresses gastric cancer progression and enhances chemosensitivity to oxaliplatin through HSP90AA1/PI3K/AKT axis. Phytomedicine 139:156441

doi: 10.1016/j.phymed.2025.156441
[37]

Li K, Chen S, Wang K, Wang Y, Xue L, et al. 2025. A peptide-centric local stability assay enables proteome-scale identification of the protein targets and binding regions of diverse ligands. Nature Methods 22:278−282

doi: 10.1038/s41592-024-02553-7
[38]

Strickland EC, Geer MA, Tran DT, Adhikari J, West GM, et al. 2013. Thermodynamic analysis of protein-ligand binding interactions in complex biological mixtures using the stability of proteins from rates of oxidation. Nature protocols 8:148−161

doi: 10.1038/nprot.2012.146
[39]

Ogburn RN, Jin L, Meng H, Fitzgerald MC. 2017. Discovery of tamoxifen and N-desmethyl tamoxifen protein targets in MCF-7 cells using large-scale protein folding and stability measurements. Journal of Proteome Research 16:4073−4085

doi: 10.1021/acs.jproteome.7b00442
[40]

Tian Y, Wan N, Zhang H, Shao C, Ding M, et al. 2022. Chemoproteomic mapping of glycolytic targetome in cancer cells. Nature Chemical Biology 19:1480−1491

doi: 10.21203/rs.3.rs-2087840/v1
[41]

Yan W, Wang D, Wan N, Wang S, Shao C, et al. 2022. Living cell-target responsive accessibility profiling reveals silibinin targeting ACSL4 for combating ferroptosis. Analytical Chemistry 94:14820−14826

doi: 10.1021/acs.analchem.2c03515
[42]

Yi J, Ye Z, Xu H, Zhang H, Cao H, et al. 2024. EGCG targeting STAT3 transcriptionally represses PLXNC1 to inhibit M2 polarization mediated by gastric cancer cell-derived exosomal miR-92b-5p. Phytomedicine 135:156137

doi: 10.1016/j.phymed.2024.156137
[43]

Ni H, Zhang Z, Lu Y, Liu Y, Zhou Y, et al. 2025. Trace component fishing strategy based on offline two-dimensional liquid chromatography combined with PRDX3-surface plasmon resonance for Uncaria alkaloids. Journal of Pharmaceutical Analysis 15:101244

doi: 10.1016/j.jpha.2025.101244
[44]

Tan QM, Li M, Zhu JM, Liao BZ, Kong LY, et al. 2025. Surface plasmon resonance guided identification of quinolone alkaloids from the fruits of Tetradium ruticarpum as FSP1 inhibitors. Journal of Natural Products 88:1919−1927

doi: 10.1021/acs.jnatprod.5c00595
[45]

Wei J, Zhang J, Hu F, Zhang W, Wu Y, et al. 2024. Anti-psoriasis effect of 18β-glycyrrhetinic acid by breaking CCL20/CCR6 axis through its vital active group targeting GUSB/ATF2 signaling. Phytomedicine 128:155524

doi: 10.1016/j.phymed.2024.155524
[46]

Huang W, Xie W, Liu H, Chen H, Ling Y, et al. 2026. Gut microbiota-derived xanthohumol protects against heatstroke by inhibiting macrophage pyroptosis in mice. Journal of Advanced Research 82:967−980

doi: 10.1016/j.jare.2025.07.031
[47]

Zhang X, Wang Q, Li Y, Ruan C, Wang S, et al. 2020. Solvent-induced protein precipitation for drug target discovery on the proteomic scale. Analytical Chemistry 92:1363−1371

doi: 10.1021/acs.analchem.9b04531
[48]

Zhang X, Wang K, Wu S, Ruan C, Li K, et al. 2022. Highly effective identification of drug targets at the proteome level by pH-dependent protein precipitation. Chemical Science 13:12403−12418

doi: 10.1039/D2SC03326G
[49]

Xu M, Moresco JJ, Chang M, Mukim A, Smith D, et al. 2018. SHMT2 and the BRCC36/BRISC deubiquitinase regulate HIV-1 Tat K63-ubiquitylation and destruction by autophagy. PLoS Pathogens 14:e1007071

doi: 10.1371/journal.ppat.1007071
[50]

Steinhart Z, Pavlovic Z, Chandrashekhar M, Hart T, Wang X, et al. 2017. Genome-wide CRISPR screens reveal a Wnt–FZD5 signaling circuit as a druggable vulnerability of RNF43-mutant pancreatic tumors. Nature Medicine 23:60−68

doi: 10.1038/nm.4219
[51]

Myszka DG, Rich RL. 2000. Implementing surface plasmon resonance biosensors in drug discovery. Pharmaceutical Science & Technology Today 3:310−317

doi: 10.1016/S1461-5347(00)00288-1
[52]

Rich RL, Myszka DG. 2000. Advances in surface plasmon resonance biosensor analysis. Current Opinion in Biotechnology 11:54−61

doi: 10.1016/s0958-1669(99)00054-3
[53]

Ward WH, Holdgate GA. 2001. Isothermal titration calorimetry in drug discovery. Progress in Medicinal Chemistry 38:309−376

doi: 10.1016/s0079-6468(08)70097-3
[54]

Chaires JB. 2008. Calorimetry and thermodynamics in drug design. Annual Review of Biophysics 37:135−151

doi: 10.1146/annurev.biophys.36.040306.132812
[55]

Uri A, Nonga OE. 2020. What is the current value of fluorescence polarization assays in small molecule screening? Expert Opinion on Drug Discovery 15:131−133

doi: 10.1080/17460441.2020.1702966
[56]

Hall MD, Yasgar A, Peryea T, Braisted JC, Jadhav A, et al. 2016. Fluorescence polarization assays in high-throughput screening and drug discovery: a review. Methods and Applications in Fluorescence 4:022001

doi: 10.1088/2050-6120/4/2/022001
[57]

Degorce F, Card A, Soh S, Trinquet E, Knapik GP, et al. 2009. HTRF: a technology tailored for drug discovery − a review of theoretical aspects and recent applications. Current Chemical Genomics 3:22−32

doi: 10.2174/1875397300903010022
[58]

Wienken CJ, Baaske P, Rothbauer U, Braun D, Duhr S. 2010. Protein-binding assays in biological liquids using microscale thermophoresis. Nature Communications 1:100

doi: 10.1038/ncomms1093
[59]

Seidel SA, Dijkman PM, Lea WA, van den Bogaart G, Jerabek-Willemsen M, et al. 2013. Microscale thermophoresis quantifies biomolecular interactions under previously challenging conditions. Methods 59:301−315

doi: 10.1016/j.ymeth.2012.12.005
[60]

Schenone M, Dančík V, Wagner BK, Clemons PA. 2013. Target identification and mechanism of action in chemical biology and drug discovery. Nature Chemical Biology 9:232−240

doi: 10.1038/nchembio.1199
[61]

Maveyraud L, Mourey L. 2020. Protein X-ray crystallography and drug discovery. Molecules 25:1030

doi: 10.3390/molecules25051030
[62]

Savitski MM, Reinhard FB, Franken H, Werner T, Savitski MF, et al. 2014. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346:1255784

doi: 10.1126/science.1255784
[63]

Lei B, Zhang M, Shi X, Feng N, Yin J, et al. 2025. Ganoderic acid T, a novel activator of pyruvate carboxylase, exhibits potent anti-liver cancer activity. Metabolism 170:156321

doi: 10.1016/j.metabol.2025.156321
[64]

Moore JD. 2015. The impact of CRISPR−Cas9 on target identification and validation. Drug Discovery Today 20:450−457

doi: 10.1016/j.drudis.2014.12.016
[65]

Gao K, Zhang W, Xu D, Zhao M, Tao X, et al. 2025. Chikusetsusaponin IVa targeted YAP as an inhibitor to attenuate liver fibrosis and hepatic stellate cell activation. Chinese Medicine 20:36

doi: 10.1186/s13020-025-01090-5
[66]

Zhao J, Tang Z, Selvaraju M, Johnson KA, Douglas JT, et al. 2022. Cellular target deconvolution of small molecules using a selection-based genetic screening platform. ACS Central Science 8:1424−1434

doi: 10.1021/acscentsci.2c00609
[67]

Sinha S, Sinha N, Perales M, Tarrab A, Nguyen T, et al. 2025. DeepTarget predicts anti-cancer mechanisms of action of small molecules by integrating drug and genetic screens. npj Precision Oncology 9:340

doi: 10.1038/s41698-025-01111-4
[68]

Li D, Yang C, Zhu JZ, Lopez E, Zhang T, et al. 2022. Berberine remodels adipose tissue to attenuate metabolic disorders by activating sirtuin 3. Acta Pharmacologica Sinica 43:1285−1298

doi: 10.1038/s41401-021-00736-y
[69]

Ito T, Ando H, Suzuki T, Ogura T, Hotta K, et al. 2010. Identification of a primary target of thalidomide teratogenicity. Science 327:1345−1350

doi: 10.1126/science.1177319
[70]

Lopez-Girona A, Mendy D, Ito T, Miller K, Gandhi AK, et al. 2012. Cereblon is a direct protein target for immunomodulatory and antiproliferative activities of lenalidomide and pomalidomide. Leukemia 26:2326−2335

doi: 10.1038/leu.2012.119
[71]

Krönke J, Udeshi ND, Narla A, Grauman P, Hurst SN, et al. 2014. Lenalidomide causes selective degradation of IKZF1 and IKZF3 in multiple myeloma cells. Science 343:301−305

doi: 10.1126/science.1244851
[72]

Lu G, Middleton RE, Sun H, Naniong M, Ott CJ, et al. 2014. The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins. Science 343:305−309

doi: 10.1126/science.1244917
[73]

Heffner CS, Herbert Pratt C, Babiuk RP, Sharma Y, Rockwood SF, et al. 2012. Supporting conditional mouse mutagenesis with a comprehensive cre characterization resource. Nature Communications 3:1218

doi: 10.1038/ncomms2186
[74]

Kim H, Kim M, Im SK, Fang S. 2018. Mouse Cre-LoxP system: general principles to determine tissue-specific roles of target genes. Laboratory Animal Research 34:147−159

doi: 10.5625/lar.2018.34.4.147
[75]

Tabana Y, Babu D, Fahlman R, Siraki AG, Barakat K. 2023. Target identification of small molecules: an overview of the current applications in drug discovery. BMC Biotechnology 23:44

doi: 10.1186/s12896-023-00815-4
[76]

Zou M, Zhou H, Gu L, Zhang J, Fang L. 2024. Therapeutic target identification and drug discovery driven by chemical proteomics. Biology 13:555

doi: 10.3390/biology13080555
[77]

Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, et al. 2019. Comprehensive integration of single-cell data. Cell 177:1888−1902.e21

doi: 10.1016/j.cell.2019.05.031
[78]

Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, et al. 2019. Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods 16:1289−1296

doi: 10.1038/s41592-019-0619-0
[79]

Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. 2018. Deep generative modeling for single-cell transcriptomics. Nature Methods 15:1053−1058

doi: 10.1038/s41592-018-0229-2
[80]

Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, et al. 2020. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biology 21:111

doi: 10.1186/s13059-020-02015-1
[81]

Biancalani T, Scalia G, Buffoni L, Avasthi R, Lu Z, et al. 2021. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nature Methods 18:1352−1362

doi: 10.1038/s41592-021-01264-7
[82]

Browaeys R, Saelens W, Saeys Y. 2020. NicheNet: modeling intercellular communication by linking ligands to target genes. Nature Methods 17:159−162

doi: 10.1038/s41592-019-0667-5
[83]

Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, et al. 2021. Inference and analysis of cell-cell communication using CellChat. Nature Communications 12:1088

doi: 10.1038/s41467-021-21246-9
[84]

Kleshchevnikov V, Shmatko A, Dann E, Aivazidis A, King HW, et al. 2022. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology 40:661−671

doi: 10.1038/s41587-021-01139-4
[85]

Stuart T, Srivastava A, Madad S, Lareau CA, Satija R. 2021. Single-cell chromatin state analysis with Signac. Nature Methods 18:1333−1341

doi: 10.1038/s41592-021-01282-5
[86]

Granja JM, Corces MR, Pierce SE, Bagdatli ST, Choudhry H, et al. 2021. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nature Genetics 53:403−411

doi: 10.1038/s41588-021-00790-6
[87]

Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods 6:377−382

doi: 10.1038/nmeth.1315
[88]

Liao Y, Liu Z, Zhang Y, Lu P, Wen L, et al. 2023. High-throughput and high-sensitivity full-length single-cell RNA-seq analysis on third-generation sequencing platform. Cell Discovery 9:5

doi: 10.1038/s41421-022-00500-4
[89]

Wang Y, Lu H, Cheng L, Guo W, Hu Y, et al. 2024. Targeting mitochondrial dysfunction in atopic dermatitis with trilinolein: a triacylglycerol from the medicinal plant Cannabis fructus. Phytomedicine 132:155856

doi: 10.1016/j.phymed.2024.155856
[90]

Zhu Y, Zhao L, Yan W, Ma H, Zhao W, et al. 2025. Celastrol directly targets LRP1 to inhibit fibroblast-macrophage crosstalk and ameliorates psoriasis progression. Acta Pharmaceutica Sinica B 15:876−891

doi: 10.1016/j.apsb.2024.12.041
[91]

Hu J, Shi Q, Xue C, Wang Q. 2024. Berberine protects against hepatocellular carcinoma progression by regulating Intrahepatic T cell heterogeneity. Advanced Science 11:e2405182

doi: 10.1002/advs.202405182
[92]

Chen J, Zhang Q, Guo J, Gu D, Liu J, et al. 2024. Single-cell transcriptomics reveals the ameliorative effect of rosmarinic acid on diabetic nephropathy-induced kidney injury by modulating oxidative stress and inflammation. Acta Pharmaceutica Sinica B 14:1661−1676

doi: 10.1016/j.apsb.2024.01.003
[93]

Sun X, Zhou L, Wang Y, Deng G, Cao X, et al. 2023. Single-cell analyses reveal cannabidiol rewires tumor microenvironment via inhibiting alternative activation of macrophage and synergizes with anti-PD-1 in colon cancer. Journal of Pharmaceutical Analysis 13:726−744

doi: 10.1016/j.jpha.2023.04.013
[94]

Wang M, Yin F, Li P, Han J, Zheng Y, et al. 2026. Spatially resolved multi-omics reveals that paeoniflorin ameliorates IgA nephropathy via Oat, Aco1 and Fh-mediated metabolic reprogramming and tubuloimmune crosstalk. Phytomedicine 156:158263

doi: 10.1016/j.phymed.2026.158263
[95]

Orsburn BC, Yuan Y, Bumpus NN. 2022. Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry. Nature Communications 13:7246

doi: 10.1038/s41467-022-34919-w
[96]

Bennett HM, Stephenson W, Rose CM, Darmanis S. 2023. Single-cell proteomics enabled by next-generation sequencing or mass spectrometry. Nature Methods 20:363−374

doi: 10.1038/s41592-023-01791-5
[97]

Gatto L, Aebersold R, Cox J, Demichev V, Derks J, et al. 2023. Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nature Methods 20:375−386

doi: 10.1038/s41592-023-01785-3
[98]

Huffman RG, Leduc A, Wichmann C, Di Gioia M, Borriello F, et al. 2023. Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics. Nature Methods 20:714−722

doi: 10.1038/s41592-023-01830-1
[99]

Budnik B, Levy E, Harmange G, Slavov N. 2018. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biology 19:161

doi: 10.1186/s13059-018-1547-5
[100]

Schoof EM, Furtwängler B, Üresin N, Rapin N, Savickas S, et al. 2021. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies. Nature Communications 12:3341

doi: 10.1038/s41467-021-23667-y
[101]

Woo J, Williams SM, Markillie LM, Feng S, Tsai CF, et al. 2021. Author Correction: High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip. Nature Communications 12:7075

doi: 10.1101/2021.02.17.431689
[102]

Derks J, Leduc A, Wallmann G, Huffman RG, Willetts M, et al. 2023. Increasing the throughput of sensitive proteomics by plexDIA. Nature Biotechnology 41:50−59

doi: 10.1038/s41587-022-01389-w
[103]

Thielert M, Itang EC, Ammar C, Rosenberger FA, Bludau I, et al. 2023. Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel. Molecular Systems Biology 19:e11503

doi: 10.1101/2022.12.02.518917
[104]

Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, et al. 2017. Simultaneous epitope and transcriptome measurement in single cells. Nature Methods 14:865−868

doi: 10.1038/nmeth.4380
[105]

Gerritsen JS, White FM. 2021. Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells. Expert Review of Proteomics 18:661−674

doi: 10.1080/14789450.2021.1976152
[106]

Blair JD, Hartman A, Zenk F, Wahle P, Brancati G, et al. 2025. Phospho-seq: integrated, multi-modal profiling of intracellular protein dynamics in single cells. Nature Communications 16:1346

doi: 10.1038/s41467-025-56590-7
[107]

Chen X, Liu J, Lu P, Zhou J, Jiang L, et al. 2025. Unraveling traditional Chinese medicine with single-cell RNA sequencing: current applications and future frontiers. Phytomedicine 149:157556

doi: 10.1016/j.phymed.2025.157556
[108]

Wang Y, Meng L, Su S, Zhao Y, Hu X, et al. 2025. Artemisia annua-derived extracellular vesicles reprogram breast tumor immune microenvironment via altering macrophage polarization and synergizing recruitment of T lymphocytes. Chinese Medicine 20:149

doi: 10.1186/s13020-025-01210-1
[109]

Zhao FJ, Wang F, Qin C, Ye LL. 2026. Single cell profiling of ER stress in coronary artery disease and therapeutic mechanisms of Ginkgo biloba extract. Scientific Reports 16:14508

doi: 10.1038/s41598-026-44541-1
[110]

Parolo S, Mariotti F, Bora P, Carboni L, Domenici E. 2023. Single-cell-led drug repurposing for Alzheimer's disease. Scientific Reports 13:222

doi: 10.1038/s41598-023-27420-x
[111]

Ali MS, Alqahtani T, Shmrany HA, Gupta G, Goh KW, et al. 2026. Artificial Intelligence in drug discovery and development: transforming pharmaceutical innovation. Drug Development Research 87:e70281

doi: 10.1002/ddr.70281
[112]

Mak KK, Pichika MR. 2019. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today 24:773−780

doi: 10.1016/j.drudis.2018.11.014
[113]

Niazi SK, Mariam Z. 2025. Artificial intelligence in drug development: reshaping the therapeutic landscape. Therapeutic Advances in Drug Safety 16:20420986251321704

doi: 10.1177/20420986251321704
[114]

Asfand-e-yar M, Hashir Q, Ali Shah A, Malik HAM, Alourani A, et al. 2024. Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events. Scientific Reports 14:4076

doi: 10.1038/s41598-024-54409-x
[115]

Perdomo-Quinteiro P, Belmonte-Hernández A. 2024. Knowledge Graphs for drug repurposing: a review of databases and methods. Briefings in Bioinformatics 25:bbae461

doi: 10.1093/bib/bbae461
[116]

Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, et al. 2023. Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences 24:2026

doi: 10.3390/ijms24032026
[117]

Wei S, Sasi C, Piepenbrock J, Huynen MA, 't Hoen PAC. 2025. The use of knowledge graphs for drug repurposing: from classical machine learning algorithms to graph neural networks. Computers in Biology and Medicine 196:110873

doi: 10.1016/j.compbiomed.2025.110873
[118]

Zhang K, Yang X, Wang Y, Yu YF, Huang N, et al. 2025. Artificial intelligence in drug development. Nature Medicine 31:45−59

doi: 10.1038/s41591-024-03434-4
[119]

Goldstein I, Lue TF, Padma-Nathan H, Rosen RC, Steers WD, et al. 2002. Oral sildenafil in the treatment of erectile dysfunction. Journal of Urology 167:1197−1203

doi: 10.1016/S0022-5347(02)80386-X
[120]

Shim JS, Liu JO. 2014. Recent advances in drug repositioning for the discovery of new anticancer drugs. International Journal of Biological Sciences 10:654−663

doi: 10.7150/ijbs.9224
[121]

Shagufta, Ahmad I. 2018. Tamoxifen a pioneering drug: an update on the therapeutic potential of tamoxifen derivatives. European Journal of Medicinal Chemistry 143:515−531

doi: 10.1016/j.ejmech.2017.11.056
[122]

Davies C, Pan H, Godwin J, Gray R, Arriagada R, et al. 2013. Long-term effects of continuing adjuvant tamoxifen to 10 years versus stopping at 5 years after diagnosis of oestrogen receptor-positive breast cancer: ATLAS, a randomised trial. The Lancet 381:805−816

doi: 10.1016/S0140-6736(12)61963-1
[123]

Zielińska A, Fornalik M, Szczepaniak M, Gimla M, Lemańska A, et al. 2026. Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing. Briefings in Bioinformatics 27:bbag203

doi: 10.1093/bib/bbag203
[124]

Huang K, Chandak P, Wang Q, Havaldar S, Vaid A, et al. 2024. A foundation model for clinician-centered drug repurposing. Nature Medicine 30:3601−3613

doi: 10.1038/s41591-024-03233-x
[125]

Zeng X, Song X, Ma T, Pan X, Zhou Y, et al. 2020. Repurpose open data to discover therapeutics for COVID-19 using deep learning. Journal of Proteome Research 19:4624−4636

doi: 10.1021/acs.jproteome.0c00316
[126]

Arora S, Mittal A, Duari S, Chauhan S, Dixit NK, et al. 2025. Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend. Nature Aging 5:144−161

doi: 10.1038/s43587-024-00763-4
[127]

Dong Y, Xiao X, Zhuang XX, Wu W, Wang ZY, et al. 2026. DeepDrugDiscovery identifies blood-brain barrier permeable autophagy enhancers for Alzheimer's disease. Nature Biomedical Engineering

doi: 10.1038/s41551-026-01667-x
[128]

Sun Y, Liu S, Chen L, Zhou Z, Yin X, et al. 2025. AI-driven discovery of dual antiaging and anti-AD therapeutics via PROTAC target deconvolution of a super-enhancer-regulated axis. Science Advances 11:eadz9283

doi: 10.1126/sciadv.adz9283
[129]

Xing J, Tan M, Leshchiner D, Sun M, Abdelgied M, et al. 2026. Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes. Cell 189:2556−2572.e19

doi: 10.1016/j.cell.2026.02.016
[130]

Talkington AM, Cao Y, Kearsley AJ, Lai SK. 2025. Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling. Advanced Drug Delivery Reviews 227:115716

doi: 10.1016/j.addr.2025.115716
[131]

Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, et al. 2023. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics 15:1916

doi: 10.3390/pharmaceutics15071916
[132]

Mavroudis PD, Teutonico D, Abos A, Pillai N. 2023. Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules. Frontiers in Systems Biology 3:1180948

doi: 10.3389/fsysb.2023.1180948
[133]

Habiballah S, Reisfeld B. 2023. Adapting physiologically-based pharmacokinetic models for machine learning applications. Scientific Reports 13:14934

doi: 10.1038/s41598-023-42165-3
[134]

Jia X, Teutonico D, Dhakal S, Psarellis YM, Abos A, et al. 2025. Application of machine learning and mechanistic modeling to predict intravenous pharmacokinetic profiles in humans. Journal of Medicinal Chemistry 68:7737−7750

doi: 10.1021/acs.jmedchem.5c00340
[135]

Chou WC, Chen Q, Yuan L, Cheng YH, He C, et al. 2023. An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice. Journal of Controlled Release 361:53−63

doi: 10.1016/j.jconrel.2023.07.040
[136]

Daina A, Zoete V. 2024. Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning. Communications Chemistry 7:105

doi: 10.1038/s42004-024-01179-2
[137]

Rao M, McDuffie E, Sachs C. 2023. Artificial intelligence/machine learning-driven small molecule repurposing via off-target prediction and transcriptomics. Toxics 11:875

doi: 10.3390/toxics11100875
[138]

Jiang S, Liu K, Jiang T, Li H, Wei X, et al. 2025. Harnessing artificial intelligence to identify Bufalin as a molecular glue degrader of estrogen receptor alpha. Nature Communications 16:7854

doi: 10.1038/s41467-025-62288-7
[139]

Liu Y, Ye J, Fan Z, Wu X, Zhang Y, et al. 2025. Ginkgetin alleviates inflammation and senescence by targeting STING. Advanced Science 12:e2407222

doi: 10.1002/advs.202407222
[140]

Sun Q, Wang H, Xie J, Wang L, Mu J, et al. 2025. Computer-aided drug discovery for undruggable targets. Chemical Reviews 125:6309−6365

doi: 10.1021/acs.chemrev.4c00969
[141]

Alipourgivi F, Su J, Lu T. 2025. Cracking PRMT5: mechanistic insights, clinical advances, and AI-driven strategies. Cancer Letters 634:218075

doi: 10.1016/j.canlet.2025.218075
[142]

Abramson J, Adler J, Dunger J, Evans R, Green T, et al. 2024. Addendum: accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 636:E4

doi: 10.55277/researchhub.zto7x62j
[143]

Jumper J, Evans R, Pritzel A, Green T, Figurnov M, et al. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596:583−589

doi: 10.1038/s41586-021-03819-2
[144]

Hu Q, Cao Y, Ren P, Zhang X, Li F, et al. 2026. DeepDegradome: a structure-aware deep learning framework for PROTAC and ligand generation against protein targets. Proceedings of the National Academy of Sciences of the United States of America 123:e2518248123

doi: 10.1073/pnas.2518248123
[145]

Jia Y, Gao B, Tan J, Zheng J, Hong X, et al. 2026. Deep contrastive learning enables genome-wide virtual screening. Science 391:eads9530

doi: 10.1126/science.ads9530
[146]

Smer-Barreto V, Quintanilla A, Elliott RJR, Dawson JC, Sun J, et al. 2023. Discovery of senolytics using machine learning. Nature Communications 14:3445

doi: 10.1038/s41467-023-39120-1
[147]

Yoo H, Han SJ, Lee JE, Cho C, Hong D, et al. 2026. Discovery of natural RORγt inhibitor using machine learning, virtual screening, and in vivo validation. Journal of Advanced Research 84:1059−1071

doi: 10.1016/j.jare.2025.09.004
[148]

Liu Y, Zhu K, Peng W, Liu Z, Mao X. 2026. Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications. Signal Transduction and Targeted Therapy 11:210

doi: 10.1038/s41392-026-02631-6
[149]

Sabit H, Yadav AK, Salimy S, Sakr A, Abdel-Ghany S, et al. 2026. Integrating multi-omics and artificial intelligence for personalized breast cancer management: a guide to clinicians. Cancer Letters 649:218468

doi: 10.1016/j.canlet.2026.218468
[150]

Cheng Y, Su Y, Fan Y, Yang Y, Chen X, et al. 2026. Aligned cross-modal integration and regulatory heterogeneity characterization of single-cell multiomic data with deep contrastive learning. Genome Medicine 18:10

doi: 10.1186/s13073-025-01586-7
[151]

Wang Y, Zhou Z, Liu W, Zhang P, Cheng Y, et al. 2026. Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA. Nature Communications 17:6459

doi: 10.1038/s41467-026-73055-7
[152]

Johnson TS, Yu CY, Huang Z, Xu S, Wang T, et al. 2022. Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease. Genome Medicine 14:11

doi: 10.1186/s13073-022-01012-2
[153]

Jolasun Y, Song K, Zheng Y, Wang J, Fonseca GJ, et al. 2025. SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation. Nature Communications 16:11271

doi: 10.1038/s41467-025-66162-4
[154]

Li C, Shao X, Zhang S, Wang Y, Jin K, et al. 2024. scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network. Cell Reports Medicine 5:101568

doi: 10.1016/j.xcrm.2024.101568
[155]

Suter RK, Jermakowicz AM, Veeramachaneni R, D'Antuono M, Zhang L, et al. 2026. Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations. Nature Communications 17:99

doi: 10.1038/s41467-025-67783-5
[156]

Wang T, Pan Y, Ju F, Zheng S, Liu C, et al. 2025. CellNavi predicts genes directing cellular transitions by learning a gene graph-enhanced cell state manifold. Nature Cell Biology 27:1863−1874

doi: 10.1038/s41556-025-01755-1
[157]

Bunne C, Roohani Y, Rosen Y, Gupta A, Zhang X, et al. 2024. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187:7045−7063

doi: 10.1016/j.cell.2024.11.015