{"id":64593,"date":"2026-06-06T13:48:56","date_gmt":"2026-06-06T13:48:56","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/64593\/"},"modified":"2026-06-06T13:48:56","modified_gmt":"2026-06-06T13:48:56","slug":"multi-omics-and-artificial-intelligence-for-precision-drug-discovery-and-potential-clinical-applications","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/64593\/","title":{"rendered":"Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications"},"content":{"rendered":"

Minikel, E. V., Painter, J. L., Dong, C. C. & Nelson, M. R. Refining the impact of genetic evidence on clinical success. Nature 629, 624\u2013629 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ghosh, S. et al. Artificial intelligence applied to \u2018omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment. Gut 74, 295\u2013311 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

He, X. et al. Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Semin. Cancer Biol. 88, 187\u2013200 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bessadok, A., Mahjoub, M. A. & Rekik, I. Graph neural networks in network neuroscience. IEEE Trans. Pattern Anal. Mach. Intell. 45, 5833\u20135848 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

He, X. H. et al. AlphaFold2 versus experimental structures: evaluation on G protein-coupled receptors. Acta Pharmacol. Sin. 44, 1\u20137 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wenteler, A. et al. AI approaches for the discovery and validation of drug targets. Camb. Prism. Precis. Med. 2, e7 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shen, X. et al. Nonlinear dynamics of multi-omics profiles during human aging. Nat. Aging 4, 1619\u20131634 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hai, L., Jiang, Z., Zhang, H. & Sun, Y. From multi-omics to predictive biomarker: AI in tumor microenvironment. Front. Immunol. 15, 1514977 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wu, Y. & Xie, L. AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships. Comput. Struct. Biotechnol. J. 27, 265\u2013277 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21, 1470\u20131480 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, J. et al. Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer. Semin. Cancer Biol. 91, 35\u201349 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Giudice, V. et al. Aptamer-based proteomics of serum and plasma in acquired aplastic anemia. Exp. Hematol. 68, 38\u201350 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Saettini, F. et al. Biallelic PI4KA mutations disrupt B-cell metabolism and cause B-cell lymphopenia and hypogammaglobulinemia. J. Clin. Immunol. 45, 15 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, W. et al. Multi-omics research strategies in ischemic stroke: a multidimensional perspective. Ageing Res. Rev. 81, 101730 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, H. et al. From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach. Brief. Bioinform. 24, bbac528 (2023).<\/p>\n

Jiang, W., Ye, W., Tan, X. & Bao, Y. J. Network-based multi-omics integrative analysis methods in drug discovery: a systematic review. BioData Min. 18, 27 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zielinski, J. M., Luke, J. J., Guglietta, S. & Krieg, C. High Throughput multi-omics approaches for clinical trial evaluation and drug discovery. Front. Immunol. 12, 590742 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lepp\u00e4, A. M. et al. Single-cell multiomics analysis reveals dynamic clonal evolution and targetable phenotypes in acute myeloid leukemia with complex karyotype. Nat. Genet. 56, 2790\u20132803 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nishide, M. et al. Single-cell multi-omics analysis identifies two distinct phenotypes of newly-onset microscopic polyangiitis. Nat. Commun. 14, 5789 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Baalbaki, N. et al. The omics landscape of long COVID\u2014a comprehensive systematic review to advance biomarker, target and drug discovery. Allergy 80, 932\u2013948 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rasooly, D., Pereira, A. C. & Joseph, J. Drug discovery and development for heart failure using multi-omics approaches. Int. J. Mol. Sci. 26, 2703 (2025).<\/p>\n

Pacini, C. et al. A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization. Cancer Cell 42, 301\u2013316.e309 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bayat, A. et al. New insights into colorectal cancer through the lens of precision oncology and personalized medicine: multi-omics helps aging of predisposed people. Curr. Aging Sci. 18, 193\u2013210 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, C. et al. Applications of multi-omics analysis in human diseases. MedComm 4, e315 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bai, Y. et al. Identification of drug targets for Sj\u00f6gren\u2019s syndrome: multi-omics Mendelian randomization and colocalization analyses. Front. Immunol. 15, 1419363 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Huang, X. et al. Single-cell systems pharmacology identifies development-driven drug response and combination therapy in B cell acute lymphoblastic leukemia. Cancer Cell 42, 552\u2013567.e556 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Si, S., Liu, H., Xu, L. & Zhan, S. Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome. Genome Med. 16, 84 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mondal, R. et al. Applications of clustered regularly interspaced short palindromic repeats (CRISPR) as a genetic scalpel for the treatment of cancer: a translational narrative review. Cureus 15, e50031 (2023).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kampmann, M. CRISPR-based functional genomics for neurological disease. Nat. Rev. Neurol. 16, 465\u2013480 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

MacLeod, G. et al. Genome-wide CRISPR-Cas9 screens expose genetic vulnerabilities and mechanisms of temozolomide sensitivity in glioblastoma stem cells. Cell Rep. 27, 971\u2013986.e979 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

McLean, B. et al. A CRISPR path to finding vulnerabilities and solving drug resistance: targeting the diverse cancer landscape and its ecosystem. Adv. Genet. 3, 2200014 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jin, Y. et al. Application and progress of CRISPR\/Cas9 gene editing in B-cell lymphoma: a narrative review. Transl. Cancer Res. 13, 1584\u20131595 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Katti, A. et al. CRISPR in cancer biology and therapy. Nat. Rev. Cancer 22, 259\u2013279 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xu, Z. et al. Structural variants drive context-dependent oncogene activation in cancer. Nature 612, 564\u2013572 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bian, S. et al. Genetically engineered cerebral organoids model brain tumor formation. Nat. Methods 15, 631\u2013639 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, S., Zhang, Z. & Han, L. 3D spheroids propel tumor characterization. Trends Cancer 6, 622\u2013624 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Goodwin, R. J. A., Platz, S. J., Reis-Filho, J. S. & Barry, S. T. Accelerating drug development using spatial multi-omics. Cancer Discov. 14, 620\u2013624 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tang, W. H. W. & Koenig, W. Multiomics insights to accelerate drug development: will they hold their promises? J. Am. Coll. Cardiol. 82, 1932\u20131935 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, F. & Barrero, C. A. Multi-omics analysis identified drug repurposing targets for chronic obstructive pulmonary disease. Int. J. Mol. Sci. 25, 11106 (2024).<\/p>\n

Zhao, Y. et al. Diverse alterations associated with resistance to KRAS(G12C) inhibition. Nature 599, 679\u2013683 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fujino, T. et al. Sensitivity and resistance of MET exon 14 mutations in lung cancer to eight MET tyrosine kinase inhibitors in vitro. J. Thorac. Oncol. 14, 1753\u20131765 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Baechler, S. A. et al. The mitochondrial type IB topoisomerase drives mitochondrial translation and carcinogenesis. Nat. Commun. 10, 83 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhao, H. et al. APC\/C-regulated CPT1C promotes tumor progression by upregulating the energy supply and accelerating the G1\/S transition. Cell Commun. Signal 22, 283 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Servetto, A., Formisano, L. & Arteaga, C. L. FGFR signaling and endocrine resistance in breast cancer: challenges for the clinical development of FGFR inhibitors. Biochim Biophys. Acta Rev. Cancer 1876, 188595 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Du, P. et al. Advances in integrated multi-omics analysis for drug-target identification. Biomolecules. 14, 692 (2024).<\/p>\n

Zhu, Q. et al. Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines. Nat. Commun. 14, 8170 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liang, W. et al. An integrated multi-omics analysis reveals osteokines involved in global regulation. Cell Metab. 36, 1144\u20131163.e1147 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, Y. et al. Unraveling the spatial organization and development of human thymocytes through integration of spatial transcriptomics and single-cell multi-omics profiling. Nat. Commun. 15, 7784 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494\u2013515 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Baysoy, A., Bai, Z., Satija, R. & Fan, R. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695\u2013713 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Takei, Y. et al. Spatial multi-omics reveals cell-type-specific nuclear compartments. Nature 641, 1037\u20131047 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, X. et al. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. J. Hematol. Oncol. 17, 72 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sun, C. et al. Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer. Nat. Commun. 14, 2692 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, Y. et al. Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics. Cancer Cell 43, 905\u2013924.e906 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hsieh, W. C. et al. Spatial multi-omics analyses of the tumor immune microenvironment. J. Biomed. Sci. 29, 96 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Qin, Y. et al. Cuproptosis correlates with immunosuppressive tumor microenvironment based on pan-cancer multiomics and single-cell sequencing analysis. Mol. Cancer 22, 59 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mars, R. A. T. et al. Longitudinal multi-omics reveals subset-specific mechanisms underlying irritable bowel syndrome. Cell 182, 1460\u20131473.e1417 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, Q. et al. Implications of gut microbiota-mediated epigenetic modifications in intestinal diseases. Gut Microbes 17, 2508426 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zou, F. et al. Effects of short-chain fatty acids in inhibiting HDAC and activating p38 MAPK are critical for promoting B10 cell generation and function. Cell Death Dis. 12, 582 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Donohoe, D. R. et al. A gnotobiotic mouse model demonstrates that dietary fiber protects against colorectal tumorigenesis in a microbiota- and butyrate-dependent manner. Cancer Discov. 4, 1387\u20131397 (2014).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sanchez, H. N. et al. B cell-intrinsic epigenetic modulation of antibody responses by dietary fiber-derived short-chain fatty acids. Nat. Commun. 11, 60 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bendixen, L., Jensen, T. I. & Bak, R. O. CRISPR-Cas-mediated transcriptional modulation: the therapeutic promises of CRISPRa and CRISPRi. Mol. Ther. 31, 1920\u20131937 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

de Bakker, V., Liu, X., Bravo, A. M. & Veening, J. W. CRISPRi-seq for genome-wide fitness quantification in bacteria. Nat. Protoc. 17, 252\u2013281 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Santinha, A. J. et al. Transcriptional linkage analysis with in vivo AAV-Perturb-seq. Nature 622, 367\u2013375 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Replogle, J. M. et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 185, 2559\u20132575.e2528 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hou, J. et al. Integrated multi-omics analyses identify anti-viral host factors and pathways controlling SARS-CoV-2 infection. Nat. Commun. 15, 109 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, L. et al. Refining antipsychotic treatment strategies in schizophrenia: discovery of genetic biomarkers for enhanced drug response prediction. Mol. Psychiatry 30, 2362\u20132371 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Heffel, M. G. et al. Temporally distinct 3D multi-omic dynamics in the developing human brain. Nature 635, 481\u2013489 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Richetto, J. & Meyer, U. Epigenetic modifications in schizophrenia and related disorders: molecular scars of environmental exposures and source of phenotypic variability. Biol. Psychiatry 89, 215\u2013226 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Guo, L. K. et al. Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis. Mil. Med. Res. 10, 24 (2023).<\/p>\n

CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, W., Liu, C., Li, Z. & Cui, M. Multi-omic biomarkers associated with multiple sclerosis: from Mendelian randomization to drug prediction. Sci. Rep. 15, 9421 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Han, Q. J. et al. PTGES2 and RNASET2 identified as novel potential biomarkers and therapeutic targets for basal cell carcinoma: insights from proteome-wide mendelian randomization, colocalization, and MR-PheWAS analyses. Front. Pharmacol. 15, 1418560 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xie, J. et al. Identification of genetic association between mitochondrial dysfunction and knee osteoarthritis through integrating multi-omics: a summary data-based Mendelian randomization study. Clin. Rheumatol. 43, 3487\u20133496 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shi, K. et al. Identification of potential therapeutic targets for nonischemic cardiomyopathy in European ancestry: an integrated multiomics analysis. Cardiovasc. Diabetol. 23, 338 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rapicavoli, R. V., Alaimo, S., Ferro, A. & Pulvirenti, A. Computational methods for drug repurposing. Adv. Exp. Med. Biol. 1361, 119\u2013141 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Aldea, M. et al. Precision medicine in the era of multi-omics: can the data tsunami guide rational treatment decision? ESMO Open 8, 101642 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lee, S. Y. et al. A proteotranscriptomic-based computational drug-repositioning method for Alzheimer\u2019s disease. Front. Pharmacol. 10, 1653 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xu, J. et al. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer\u2019s disease. Cell Rep. 41, 111717 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Albuquerque, A. M. et al. Effect of tocilizumab, sarilumab, and baricitinib on mortality among patients hospitalized for COVID-19 treated with corticosteroids: a systematic review and meta-analysis. Clin. Microbiol Infect. 29, 13\u201321 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Karampitsakos, T. et al. Tocilizumab versus baricitinib in hospitalized patients with severe COVID-19: an open label, randomized controlled trial. Clin. Microbiol. Infect. 29, 372\u2013378 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Guo, H. et al. Multi-omics analyses reveal that HIV-1 alters CD4(+) T cell immunometabolism to fuel virus replication. Nat. Immunol. 22, 423\u2013433 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, S. et al. Potential anti-liver cancer targets and mechanisms of kaempferitrin based on network pharmacology, molecular docking and experimental verification. Comput. Biol. Med. 178, 108693 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nguyen, T. M. et al. DeCoST: a new approach in drug repurposing from control system theory. Front. Pharmacol. 9, 583 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, X. et al. Network pharmacology approaches for research of traditional Chinese medicines. Chin. J. Nat. Med. 21, 323\u2013332 (2023).<\/p>\n

PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, S. et al. Integrated bioinformatics and multiomics reveal Liupao tea extract alleviating NAFLD via regulating hepatic lipid metabolism and gut microbiota. Phytomedicine 132, 155834 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ye, J. et al. Multi-omics and network pharmacology study reveals the effects of Dengzhan Shengmai capsule against neuroinflammatory injury and thrombosis induced by ischemic stroke. J. Ethnopharmacol. 305, 116092 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mokou, M. et al. A drug repurposing pipeline based on bladder cancer integrated proteotranscriptomics signatures. Methods Mol. Biol. 2684, 59\u201399 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Timilsina, S. et al. The antidepressant imipramine inhibits breast cancer growth by targeting estrogen receptor signaling and DNA repair events. Cancer Lett. 540, 215717 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kandela, I. & Aird, F. Replication study: discovery and preclinical validation of drug indications using compendia of public gene expression data. eLife 6, e17044 (2017).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lee, S. et al. High-throughput identification of repurposable neuroactive drugs with potent anti-glioblastoma activity. Nat. Med. 30, 3196\u20133208 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Daher, A. & de Groot, J. Rapid identification and validation of novel targeted approaches for glioblastoma: a combined ex vivo-in vivo pharmaco-omic model. Exp. Neurol. 299, 281\u2013288 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med. 3, 96ra76 (2011).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Aydin, B. et al. Epigenomic and transcriptomic landscaping unraveled candidate repositioned therapeutics for non-functioning pituitary neuroendocrine tumors. J. Endocrinol. Invest. 46, 727\u2013747 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Boso, D. et al. Anti-VEGF therapy selects for clones resistant to glucose starvation in ovarian cancer xenografts. J. Exp. Clin. Cancer Res. 42, 196 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665\u20131681.e1618 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Miller, B. F. et al. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nat. Commun. 13, 2339 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, S. L. et al. Acarbose enhances the efficacy of immunotherapy against solid tumours by modulating the gut microbiota. Nat. Metab. 6, 1991\u20132009 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mayor-Ruiz, C. et al. Rational discovery of molecular glue degraders via scalable chemical profiling. Nat. Chem. Biol. 16, 1199\u20131207 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pan, S. et al. Metabolomics-driven approaches for identifying therapeutic targets in drug discovery. MedComm 5, e792 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Serral, F. et al. From genome to drugs: new approaches in antimicrobial discovery. Front. Pharmacol. 12, 647060 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhong-gen, Z. Advances in biosynthesis and regulation of the active ingredient of Salvia miltiorrhiza based on multi-omics approach. Acta Pharm. Sin. 55(12), 2892\u20132903 (2020).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yuan, Z. et al. Celastrol combats methicillin-resistant Staphylococcus aureus by targeting \u0394(1) -pyrroline-5-carboxylate dehydrogenase. Adv. Sci. 10, e2302459 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Soares, N. C. et al. Unveiling the mechanism of action of nature-inspired anti-cancer compounds using a multi-omics approach. J. Proteom. 265, 104660 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wu, L. et al. Glycyrrhiza, a commonly used medicinal herb: review of species classification, pharmacology, active ingredient biosynthesis, and synthetic biology. J. Adv. Res. 75, 249\u2013270 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, Y., Shi, Y. N., Xiang, H. & Shi, Y. M. Exploring nature\u2019s battlefield: organismic interactions in the discovery of bioactive natural products. Nat. Prod. Rep. 41, 1630\u20131651 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Palazzotto, E., Tong, Y., Lee, S. Y. & Weber, T. Synthetic biology and metabolic engineering of actinomycetes for natural product discovery. Biotechnol. Adv. 37, 107366 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ding, X. et al. Multiple mitochondria-targeted components screened from Sini decoction improved cardiac energetics and mitochondrial dysfunction to attenuate doxorubicin-induced cardiomyopathy. Theranostics 13, 510\u2013530 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ma, C. et al. Qijiao Shengbai Capsule alleviated leukopenia by interfering leukotriene pathway: integrated network study of multi-omics. Phytomedicine 128, 155424 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cheng, L. et al. The protective role of commensal gut microbes and their metabolites against bacterial pathogens. Gut Microbes 16, 2356275 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Castelo, J. et al. The microbiota metabolite, phloroglucinol, confers long-term protection against inflammation. Gut Microbes 16, 2438829 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, J. R. et al. Gut microbiota-derived tryptophan metabolism mediates renal fibrosis by aryl hydrocarbon receptor signaling activation. Cell Mol. Life Sci. 78, 909\u2013922 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vogel, C. F. A., Van Winkle, L. S., Esser, C. & Haarmann-Stemmann, T. The aryl hydrocarbon receptor as a target of environmental stressors\u2014 implications for pollution mediated stress and inflammatory responses. Redox Biol. 34, 101530 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, N. N. et al. Multi-kingdom microbiota analyses identify bacterial-fungal interactions and biomarkers of colorectal cancer across cohorts. Nat. Microbiol 7, 238\u2013250 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gao, W. et al. Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer. Gut Microbes 15, 2245562 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ma, A., Xin, G. & Ma, Q. The use of single-cell multi-omics in immuno-oncology. Nat. Commun. 13, 2728 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Collora, J. A. et al. Single-cell multiomics reveals persistence of HIV-1 in expanded cytotoxic T cell clones. Immunity 55, 1013\u20131031.e1017 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rausch, J. W. et al. HIV expression in infected T cell clones. Viruses 16, 108 (2024).<\/p>\n

Unterman, A. et al. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nat. Commun. 13, 440 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Anglada-Girotto, M. et al. Combining CRISPRi and metabolomics for functional annotation of compound libraries. Nat. Chem. Biol. 18, 482\u2013491 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mitchell, D. C. et al. A proteome-wide atlas of drug mechanism of action. Nat. Biotechnol. 41, 845\u2013857 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, C. et al. Mendelian randomization and genetic colocalization infer the effects of the multi-tissue proteome on 211 complex disease-related phenotypes. Genome Med. 14, 140 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, P. et al. Integrated spatial metabolomics and transcriptomics decipher the hepatoprotection mechanisms of wedelolactone and demethylwedelolactone on non-alcoholic fatty liver disease. J. Pharm. Anal. 14, 100910 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ji, S. et al. Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology. Sci. Transl. Med. 15, eadg3358 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Waldenmaier, H. E. et al. \u201cLab of the future\u201d\u2500today: fully automated system for high-throughput mass spectrometry analysis of biotherapeutics. J. Am. Soc. Mass Spectrom. 34, 1073\u20131085 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Howard, J. & Reiber, J. H. C. Automated analysis of coronary angiograms using artificial intelligence: a window into the cath lab of the future. EuroIntervention 17, 16\u201317 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xiong, Z. et al. Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches. Sci. China Life Sci. 65, 529\u2013539 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xiong, G. et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 49, W5\u2013w14 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Qiao, J. et al. AI-based R&D for frozen and thawed meat: research progress and future prospects. Compr. Rev. Food Sci. Food Saf. 23, e70016 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tong, L. et al. Integrating multi-omics data with EHR for precision medicine using advanced artificial intelligence. IEEE Rev. Biomed. Eng. 17, 80\u201397 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, Z., Guan, F., Bronk, L. & Zhao, L. Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: a multidimensional perspective. Pharm. Ther. 254, 108591 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, L., Sun, M., Wang, J. & Wan, S. Multi-omics based artificial intelligence for cancer research. Adv. Cancer Res. 163, 303\u2013356 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Qiu, Y. & Cheng, F. Artificial intelligence for drug discovery and development in Alzheimer\u2019s disease. Curr. Opin. Struct. Biol. 85, 102776 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Choi, S., Adams, A. M. & Chibale, K. Special issue: exploring the use of AI\/ML technologies in medicinal chemistry and drug discovery. ACS Med. Chem. Lett. 16, 174 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sumathi, S. et al. A review on deep learning-driven drug discovery: strategies, tools and applications. Curr. Pharm. Des. 29, 1013\u20131025 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gangwal, A. et al. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front. Pharmacol. 15, 1331062 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688\u2013702.e613 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, G. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat. Chem. Biol. 19, 1342\u20131350 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, K. et al. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59, 3370\u20133388 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Luttens, A. et al. Rapid traversal of vast chemical space using machine learning-guided docking screens. Nat. Comput. Sci. 5, 301\u2013312 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Patronov, A., Papadopoulos, K. & Engkvist, O. Has artificial intelligence impacted drug discovery? Methods Mol. Biol. 2390, 153\u2013176 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vamathevan, J. et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18, 463\u2013477 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ren, F. et al. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat. Biotechnol. 43, 63\u201375 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038\u20131040 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, X. S. et al. Multiphysical graph neural network (MP-GNN) for COVID-19 drug design. Brief. Bioinform. 23, bbac231 (2022).<\/p>\n

Wong, F. et al. An explainable deep learning platform for molecular discovery. Nat. Protoc. 20, 1020\u20131056 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fotis, C., Meimetis, N., Sardis, A. & Alexopoulos, L. G. DeepSIBA: chemical structure-based inference of biological alterations using deep learning. Mol. Omics 17, 108\u2013120 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, G. & Stokes, J. M. A brief guide to machine learning for antibiotic discovery. Curr. Opin. Microbiol. 69, 102190 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wong, F. et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature 626, 177\u2013185 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhan, H., Zhu, X., Qiao, Z. & Hu, J. Graph neural tree: a novel and interpretable deep learning-based framework for accurate molecular property predictions. Anal. Chim. Acta 1244, 340558 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Deng, D. et al. XGraphBoost: extracting graph neural network-based features for a better prediction of molecular properties. J. Chem. Inf. Model. 61, 2697\u20132705 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Olivecrona, M., Blaschke, T., Engkvist, O. & Chen, H. Molecular de-novo design through deep reinforcement learning. J. Cheminform. 9, 48 (2017).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Maziarka, \u0141 et al. Mol-CycleGAN: a generative model for molecular optimization. J. Cheminform. 12, 2 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tu, X. et al. Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model. Chin. Med. J. 138, 172\u2013184 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, N., Dong, J. & Ouyang, D. AI-directed formulation strategy design initiates rational drug development. J. Control. Release 378, 619\u2013636 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gold, E. R. & Cook-Deegan, R. AI drug development\u2019s data problem. Science 388, 131 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cyranoski, D. AI drug discovery booms in China. Nat. Biotechnol. 39, 900\u2013902 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, Y., Yang, Z. & Yao, Q. Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning. Commun. Med. 4, 59 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, Y. et al. Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network. Nat. Comput. Sci. 3, 1023\u20131033 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, F. et al. DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras. Brief. Bioinform. 25, bbae358 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Alakhdar, A., Poczos, B. & Washburn, N. Diffusion models in de novo drug design. J. Chem. Inf. Model. 64, 7238\u20137256 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wei, X. et al. Fragment-based discovery of small molecule inhibitors of the HDGFRP2 PWWP domain. FEBS Lett. 598, 2533\u20132543 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wan, X. et al. An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph. Brief. Bioinform. 23, bbac073 (2022).<\/p>\n

Zhang, Z. et al. Graph neural network approaches for drug-target interactions. Curr. Opin. Struct. Biol. 73, 102327 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Han, K. et al. A review of approaches for predicting drug-drug interactions based on machine learning. Front. Pharmacol. 12, 814858 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, N. N. et al. Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes. J. Cheminform. 14, 23 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lin, X. et al. Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction. Brief. Bioinform. 24, bbad235 (2023).<\/p>\n

Nyamabo, A. K., Yu, H. & Shi, J. Y. SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction. Brief. Bioinform. 22, bbab133 (2021).<\/p>\n

Yan, X., Gu, C., Feng, Y. & Han, J. Predicting drug-drug interaction with graph mutual interaction attention mechanism. Methods 223, 16\u201325 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dmitriev, A. V. et al. Drug-drug interaction prediction using PASS. SAR QSAR Environ. Res. 30, 655\u2013664 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yasir, M. et al. Machine learning-based drug repositioning of novel janus kinase 2 inhibitors utilizing molecular docking and molecular dynamic simulation. J. Chem. Inf. Model. 63, 6487\u20136500 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

J, S. G., P, D. & P, E. Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction – DrugSchizoNet. Comput. Methods Biomech. Biomed. Engin. 28, 170\u2013187 (2025).<\/p>\n

Zhang, Y. et al. Neural network-based approaches for biomedical relation classification: a review. J. Biomed. Inform. 99, 103294 (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shen, Z. A. et al. NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks. Brief. Bioinform. 22, bbab051 (2021).<\/p>\n

Zhao, Y. et al. Drug-drug interaction prediction: databases, web servers and computational models. Brief. Bioinform. 25, bbad445 (2023).<\/p>\n

Kavuluru, R., Rios, A. & Tran, T. Extracting drug-drug interactions with word and character-level recurrent neural networks. Proc. IEEE Int. Conf. Health. Inf. 2017, 5\u201312 (2017).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

You, Y., Lu, C., Wang, W. & Tang, C. K. Relative CNN-RNN: learning relative atmospheric visibility from images. IEEE Trans. Image Process. 28, 45\u201355 (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, J. & Guo, X. Automated detection of myocardial infarction based on an improved state refinement module for LSTM\/GRU. Artif. Intell. Med. 152, 102865 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, J. et al. Visual analytics for RNN-based deep reinforcement learning. IEEE Trans. Vis. Comput. Graph 28, 4141\u20134155 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Khaki, S., Wang, L. & Archontoulis, S. V. A CNN-RNN framework for crop yield prediction. Front. Plant Sci. 10, 1750 (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, Y. et al. A hybrid model based on neural networks for biomedical relation extraction. J. Biomed. Inform. 81, 83\u201392 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zaikis, D. & Vlahavas, I. TP-DDI: transformer-based pipeline for the extraction of Drug-Drug Interactions. Artif. Intell. Med. 119, 102153 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zaikis, D. & Vlahavas, I. TransformDDI: the transformer-based joint multi-task model for end-to-end drug-drug interaction extraction. IEEE J. Biomed. Health Inform. 29, 3045\u20133056 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

J\u00e4rvinen, E. et al. 3D spheroid primary human hepatocytes for prediction of cytochrome P450 and drug transporter induction. Clin. Pharmacol. Ther. 113, 1284\u20131294 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Col\u00f3n Ortiz, R. et al. Cocaine regulates antiretroviral therapy CNS access through pregnane-x receptor-mediated drug transporter and metabolizing enzyme modulation at the blood brain barrier. Fluids Barriers CNS 21, 5 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cao, P. Y. et al. Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability. J. Cheminform. 16, 133 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yin, H. et al. Inhibition of human UDP-glucuronosyltransferase enzyme by entrectinib: Implications for drug-drug interactions. Chem. Biol. Interact. 395, 111023 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Galetin, A., Burt, H., Gibbons, L. & Houston, J. B. Prediction of time-dependent CYP3A4 drug-drug interactions: impact of enzyme degradation, parallel elimination pathways, and intestinal inhibition. Drug Metab. Dispos. 34, 166\u2013175 (2006).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Prieto Garcia, L. et al. Physiologically based pharmacokinetic model of itraconazole and two of its metabolites to improve the predictions and the mechanistic understanding of CYP3A4 drug-drug interactions. Drug Metab. Dispos. 46, 1420\u20131433 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Han, H. et al. Employing automated machine learning (AutoML) methods to facilitate the in silico ADMET properties prediction. J. Chem. Inf. Model. 65, 3215\u20133225 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Menestrina, L. et al. Refined ADME profiles for ATC drug classes. Pharmaceutics. 17, 308 (2025).<\/p>\n

Williams, D. P. et al. Predicting drug-induced liver injury with Bayesian machine learning. Chem. Res. Toxicol. 33, 239\u2013248 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Semenova, E., Afzal, W. D. & Lazic, A. M. SE. A Bayesian neural network for toxicity prediction. Comput. Toxicol. 16, 100133 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Smith, G. F. Artificial intelligence in drug safety and metabolism. Methods Mol. Biol. 2390, 483\u2013501 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mamoshina, P., Bueno-Orovio, A. & Rodriguez, B. Dual transcriptomic and molecular machine learning predicts all major clinical forms of drug cardiotoxicity. Front. Pharmacol. 11, 639 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hammann, F., Sch\u00f6ning, V. & Drewe, J. Prediction of clinically relevant drug-induced liver injury from structure using machine learning. J. Appl. Toxicol. 39, 412\u2013419 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Joshi, P. V. M. & Mukherjee, A. A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network. J. Biomed. Inform. 132, 104122 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rao, M. et al. Artificial intelligence and machine learning models for predicting drug-induced kidney injury in small molecules. Pharmaceuticals 17, 1550 (2024).<\/p>\n

Liu, J. et al. In silico off-target profiling for enhanced drug safety assessment. Acta Pharm. Sin. B 14, 2927\u20132941 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kp Jayatunga, M. et al. How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons. Drug Discov. Today 29, 104009 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Catacutan, D. B., Alexander, J., Arnold, A. & Stokes, J. M. Machine learning in preclinical drug discovery. Nat. Chem. Biol. 20, 960\u2013973 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cai, H. et al. FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction. Brief. Bioinform. 23, bbac408 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ashe, E. C., Comeau, A. M., Zejdlik, K. & O\u2019Connell, S. P. Characterization of bacterial community dynamics of the human mouth throughout decomposition via metagenomic, metatranscriptomic, and culturing techniques. Front. Microbiol. 12, 689493 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhai, Y., Chen, L. & Deng, M. scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery. Brief. Bioinform. 24, bbad045 (2023).<\/p>\n

Wang, B. et al. STCGAN: a novel cycle-consistent generative adversarial network for spatial transcriptomics cellular deconvolution. Brief. Bioinform. 26, bbae670 (2024).<\/p>\n

Mswahili, M. E. et al. Positional embeddings and zero-shot learning using BERT for molecular-property prediction. J. Cheminform. 17, 17 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hao, M. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods 21, 1481\u20131491 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhai, Y., Chen, L. & Deng, M. scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data. Brief. Bioinform. 25, bbae188 (2024).<\/p>\n

Guo, Q., Yuan, M., Zhang, L. & Deng, M. scPLAN: a hierarchical computational framework for single transcriptomics data annotation, integration and cell-type label refinement. Brief. Bioinform. 25, bbae305 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rozera, T., Pasolli, E., Segata, N. & Ianiro, G. Machine learning and artificial intelligence in the multi-omics approach to gut microbiota. Gastroenterology. 169, 487\u2013501 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dakal, T. C., Xu, C. & Kumar, A. Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification. Front. Med. Technol. 6, 1434799 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Majidova, K. et al. Role of digital health and artificial intelligence in inflammatory bowel disease: a scoping review. Genes 12, 1465 (2021).<\/p>\n

Zheng, J. et al. Noninvasive, microbiome-based diagnosis of inflammatory bowel disease. Nat. Med. 30, 3555\u20133567 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Huang, Q., Zhang, X. & Hu, Z. Application of artificial intelligence modeling technology based on multi-omics in noninvasive diagnosis of inflammatory bowel disease. J. Inflamm. Res. 14, 1933\u20131943 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xu, Z. et al. Precision medicine in colorectal cancer: leveraging multi-omics, spatial omics, and artificial intelligence. Clin. Chim. Acta 559, 119686 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kann, B. H., Hosny, A. & Aerts, H. Artificial intelligence for clinical oncology. Cancer Cell 39, 916\u2013927 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tanaka, I., Furukawa, T. & Morise, M. The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence. Cancer Cell Int. 21, 454 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Barroso, R. A. et al. Unlocking antimicrobial peptides: in silico proteolysis and artificial intelligence-driven discovery from cnidarian omics. Molecules. 30, 550 (2025).<\/p>\n

Feng, J. et al. A synthetic antibiotic class with a deeply-optimized design for overcoming bacterial resistance. Nat. Commun. 15, 6040 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Oh, H. S. et al. Organ aging signatures in the plasma proteome track health and disease. Nature 624, 164\u2013172 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Duggan, M. R. & Walker, K. A. Organ-specific aging in the plasma proteome predicts disease. Trends Mol. Med. 30, 423\u2013424 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, T. et al. Integrating machine learning and multi-omics analysis to reveal nucleotide metabolism-related immune genes and their functional validation in ischemic stroke. Front. Immunol. 16, 1561544 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhao, Y. et al. Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke. Sci. Rep. 14, 26069 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hebron, M. et al. Discoidin domain receptor inhibition reduces neuropathology and attenuates inflammation in neurodegeneration models. J. Neuroimmunol. 311, 1\u20139 (2017).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kortagere, S. et al. Identification of novel allosteric modulators of glutamate transporter EAAT2. ACS Chem. Neurosci. 9, 522\u2013534 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, B. et al. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent. Sci. 3, 1103\u20131113 (2017).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, Y. et al. Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks. Nat. Commun. 14, 6155 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hu, J., Luo, Y. & Wang, X. Multi-omics analysis of druggable genes to facilitate Alzheimer\u2019s disease therapy: a multi-cohort machine learning study. J. Prev. Alzheimers Dis. 12, 100128 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cummings, J. et al. Alzheimer\u2019s disease drug development pipeline: 2023. Alzheimers Dement. 9, e12385 (2023).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rodriguez, S. et al. Machine learning identifies candidates for drug repurposing in Alzheimer\u2019s disease. Nat. Commun. 12, 1033 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cheng, F. et al. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer\u2019s disease. Cell Rep. Med. 5, 101379 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, Y. et al. AlzGPS: a genome-wide positioning systems platform to catalyze multi-omics for Alzheimer\u2019s drug discovery. Alzheimers Res. Ther. 13, 24 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zulhafiz, N. A., Teoh, T. C., Chin, A. V. & Chang, S. W. Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: a comprehensive review in general diseases vs Alzheimer\u2019s disease. Comput. Methods Prog. Biomed. 261, 108604 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wan, Z. et al. Applications of artificial intelligence in drug repurposing. Adv. Sci. 12, e2411325 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, V. O. K. et al. DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer\u2019s disease. Sci. Rep. 15, 2093 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shin, M. K. et al. Reducing acetylated tau is neuroprotective in brain injury. Cell 184, 2715\u20132732.e2723 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Min, S. W. et al. Critical role of acetylation in tau-mediated neurodegeneration and cognitive deficits. Nat. Med. 21, 1154\u20131162 (2015).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fang, J. et al. Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer\u2019s disease. Nat. Aging 1, 1175\u20131188 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Paranjpe, M. D., Taubes, A. & Sirota, M. Insights into computational drug repurposing for neurodegenerative disease. Trends Pharmacol. Sci. 40, 565\u2013576 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sun, X. et al. Multi-omics Mendelian randomization integrating GWAS, eQTL and pQTL data revealed GSTM4 as a potential drug target for migraine. J. Headache Pain. 25, 117 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bai, C. et al. Machine learning-enabled drug-induced toxicity prediction. Adv. Sci. 12, e2413405 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Monzel, A. S. et al. Machine learning-assisted neurotoxicity prediction in human midbrain organoids. Parkinsonism Relat. Disord. 75, 105\u2013109 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kuusisto, F. et al. Machine learning to predict developmental neurotoxicity with high-throughput data from 2D bio-engineered tissues. Proc. Int. Conf. Mach. Learn. Appl. 2019, 293\u2013298 (2019).<\/p>\n

PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhao, X. et al. Machine learning modeling and insights into the structural characteristics of drug-induced neurotoxicity. J. Chem. Inf. Model. 62, 6035\u20136045 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lee, B. et al. A deep learning approach with data augmentation to predict novel spider neurotoxic peptides. Int. J. Mol. Sci. 22, 12291 (2021).<\/p>\n

Zeng, X. et al. Accurate prediction of molecular targets using a self-supervised image representation learning framework. Res. Sq. 3, 1477870 (2022).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Parikh, J. et al. Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action. J. Pharmacokinet. Pharmacodyn. 49, 51\u201364 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cummings, J. Lessons learned from Alzheimer disease: clinical trials with negative outcomes. Clin. Transl. Sci. 11, 147\u2013152 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shaker, B. et al. LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM. Bioinformatics 37, 1135\u20131139 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Saxena, D., Sharma, A., Siddiqui, M. H. & Kumar, R. Blood brain barrier permeability prediction using machine learning techniques: an update. Curr. Pharm. Biotechnol. 20, 1163\u20131171 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nada, H. et al. Machine learning-based approach to developing potent EGFR inhibitors for breast cancer-design, synthesis, and in vitro evaluation. ACS Omega 8, 31784\u201331800 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bararia, A. et al. A multi-phase approach using supervised algorithms and clinical models to generate high-accuracy signatures for pancreatic cancer. Comput. Biol. Med. 194, 110559 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sharma, S., Singh, R., Kant, S. & Mishra, M. K. Integrating AI\/ML and multi-omics approaches to investigate the role of TNFRSF10A\/TRAILR1 and its potential targets in pancreatic cancer. Comput. Biol. Med. 193, 110432 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yan, H., Ji, X. & Li, B. Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery. Front. Immunol. 16, 1572034 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cheerla, A. & Gevaert, O. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35, i446\u2013i454 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Biswas, N. & Chakrabarti, S. Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer. Front. Oncol. 10, 588221 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chu, X. et al. Cancer stem cells: advances in knowledge and implications for cancer therapy. Signal Transduct. Target Ther. 9, 170 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

He, Z. et al. Multi-omics and tumor immune microenvironment characterization of a prognostic model based on aging-related genes in melanoma. Am. J. Cancer Res. 14, 1052\u20131070 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Abdelfattah, N. et al. Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target. Nat. Commun. 13, 767 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mehrotra, S., Sharma, S. & Pandey, R. K. A journey from omics to clinicomics in solid cancers: success stories and challenges. Adv. Protein Chem. Struct. Biol. 139, 89\u2013139 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Patkulkar, P. A., Subbalakshmi, A. R., Jolly, M. K. & Sinharay, S. Mapping spatiotemporal heterogeneity in tumor profiles by integrating high-throughput imaging and omics analysis. ACS Omega 8, 6126\u20136138 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Feng, Y. et al. Spatially organized tumor-stroma boundary determines the efficacy of immunotherapy in colorectal cancer patients. Nat. Commun. 15, 10259 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cembrowska-Lech, D. et al. An integrated multi-omics and artificial intelligence framework for advance plant phenotyping in horticulture. Biology 12, 1298 (2023).<\/p>\n

Imai, M. et al. Artificial intelligence-powered human epidermal growth factor receptor 2 and tumor microenvironment analysis in human epidermal growth factor receptor 2-amplified metastatic colorectal cancer: exploratory analysis of phase II TRIUMPH trial. JCO Precis. Oncol. 9, e2400385 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kumar, H. et al. secDrug: a pipeline to discover novel drug combinations to kill drug-resistant multiple myeloma cells using a greedy set cover algorithm and single-cell multi-omics. Blood Cancer J. 12, 39 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, W. et al. Integrating radiomics with genomics for non-small cell lung cancer survival analysis. J. Oncol. 2022, 5131170 (2022).<\/p>\n

PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Quek, C. et al. Single-cell spatial multiomics reveals tumor microenvironment vulnerabilities in cancer resistance to immunotherapy. Cell Rep. 43, 114392 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hu, T. et al. Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma. Int. J. Med. Sci. 22, 1375\u20131392 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kan, Z. et al. Real-world clinical multi-omics analyses reveal bifurcation of ER-independent and ER-dependent drug resistance to CDK4\/6 inhibitors. Nat. Commun. 16, 932 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Au Yeung, V. P. W. et al. Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity. Cell Biol. Toxicol. 40, 50 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhu, Z. et al. Two-dimensional deep learning frameworks for drug-induced cardiotoxicity detection. ACS Sens 9, 3316\u20133326 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yu, L., Xu, Z., Qiu, W. & Xiao, X. MSDSE: predicting drug-side effects based on multi-scale features and deep multi-structure neural network. Comput. Biol. Med. 169, 107812 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Uner, O. C. et al. DeepSide: a deep learning approach for drug side effect prediction. IEEE\/ACM Trans. Comput. Biol. Bioinform. 20, 330\u2013339 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wei, J. et al. GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning. Brief. Bioinform. 24, bbad247 (2023).<\/p>\n

Mason, D. M. et al. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Nat. Biomed. Eng. 5, 600\u2013612 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sinha, S. et al. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. Nat. Cancer 5, 938\u2013952 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Joshi, A., Rienks, M., Theofilatos, K. & Mayr, M. Systems biology in cardiovascular disease: a multiomics approach. Nat. Rev. Cardiol. 18, 313\u2013330 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kiessling, P. & Kuppe, C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med. 16, 14 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xu, Y. et al. An atlas of genetic scores to predict multi-omic traits. Nature 616, 123\u2013131 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ouwerkerk, W. et al. Multiomics analysis provides novel pathways related to progression of heart failure. J. Am. Coll. Cardiol. 82, 1921\u20131931 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, J. et al. Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of dilated cardiomyopathy. Sci. Transl. Med. 14, eabl5654 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616\u2013624 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shi, H. & Zhang, S. Accurate prediction of anti-hypertensive peptides based on convolutional neural network and gated recurrent unit. Interdiscip. Sci. 14, 879\u2013894 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ma, C., Zhou, Z., Liu, H. & Koslicki, D. KGML-xDTD: a knowledge graph-based machine learning framework for drug treatment prediction and mechanism description. Gigascience 12, giad057 (2022).<\/p>\n

Preuss, M. et al. Design of the coronary artery disease genome-wide replication and meta-analysis (CARDIoGRAM) study: a genome-wide association meta-analysis involving more than 22 000 cases and 60 000 controls. Circ. Cardiovasc. Genet. 3, 475\u2013483 (2010).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714\u2013719 (2010).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Linsel-Nitschke, P. et al. Genetic variation at chromosome 1p13.3 affects sortilin mRNA expression, cellular LDL-uptake and serum LDL levels which translates to the risk of coronary artery disease. Atherosclerosis 208, 183\u2013189 (2010).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57\u201363 (2009).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wu, P. Y. et al. Cardiovascular transcriptomics and epigenomics using next-generation sequencing: challenges, progress, and opportunities. Circ. Cardiovasc. Genet. 7, 701\u2013710 (2014).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mohammadi, P. et al. Genetic regulatory variation in populations informs transcriptome analysis in rare disease. Science 366, 351\u2013356 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Franz\u00e9n, O. et al. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 353, 827\u2013830 (2016).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Human genomics The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648\u2013660 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Khan, S. U. et al. PCSK9 inhibitors and ezetimibe with or without statin therapy for cardiovascular risk reduction: a systematic review and network meta-analysis. BMJ 377, e069116 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ren, Z. H. et al. DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis. J. Transl. Med. 21, 48 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Karlst\u00e4dt, A. et al. CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism. BMC Syst. Biol. 6, 114 (2012).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Edwards, L. M. et al. Genome-scale methods converge on key mitochondrial genes for the survival of human cardiomyocytes in hypoxia. Circ. Cardiovasc. Genet. 7, 407\u2013415 (2014).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zong, N. et al. Advancing efficacy prediction for EHR-based emulated trials in repurposing heart failure therapies. NPJ Digit. Med. 8, 306 (2025).<\/p>\n

Wang, L. et al. Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: the Mayo-Baylor RIGHT 10K Study. Genet. Med. 24, 1062\u20131072 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tan, H. et al. Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes. J. Adv. Res. 64, 213\u2013221 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Alles\u00f8e, R. L. et al. Author Correction: Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models. Nat. Biotechnol. 41, 1026 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shapiro, M. R. et al. Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes. Diabetologia 68, 477\u2013494 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, F. et al. Harnessing omics data for drug discovery and development in ovarian aging. Hum. Reprod. Update 31, 240\u2013268 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jusoh, A. S. et al. How generative artificial intelligence can transform drug discovery? Eur. J. Med. Chem. 295, 117825 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rabaan, A. A. et al. Omics approaches in drug development against leishmaniasis: current scenario and future prospects. Pathogens 12, 39 (2022).<\/p>\n

Hay, M. et al. Clinical development success rates for investigational drugs. Nat. Biotechnol. 32, 40\u201351 (2014).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Harrer, S., Shah, P., Antony, B. & Hu, J. Artificial intelligence for clinical trial design. Trends Pharmacol. Sci. 40, 577\u2013591 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gligorijevic, J. et al. Optimizing clinical trials recruitment via deep learning. J. Am. Med. Inform. Assoc. 26, 1195\u20131202 (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fogel, D. B. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp. Clin. Trials Commun. 11, 156\u2013164 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Persidis, A. The benefits of drug repositioning. Drug Discov. World 12, 9\u201312 (2011).<\/p>\n

Baldi Antognini, A., Novelli, M., Zagoraiou, M. & Vagheggini, A. Compound optimal allocations for survival clinical trials. Biom. J. 62, 1730\u20131746 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Perni, S., Jimenez, R. & Jagsi, R. Optimizing informed consent in cancer clinical trials. Semin. Radiat. Oncol. 33, 349\u2013357 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sarkar, C. et al. Artificial intelligence and machine learning technology driven modern drug discovery and development. Int. J. Mol. Sci. 24, 2026 (2023).<\/p>\n

van der Lee, M. & Swen, J. J. Artificial intelligence in pharmacology research and practice. Clin. Transl. Sci. 16, 31\u201336 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wilczok, D. & Zhavoronkov, A. Progress, pitfalls, and impact of AI-driven clinical trials. Clin. Pharmacol. Ther. 117, 887\u2013890 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Aguado, B. A. et al. The future of sex and gender in research. Cell 187, 1354\u20131357 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bewley, S., McCartney, M., Meads, C. & Rogers, A. Sex, gender, and medical data. BMJ 372, n735 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Boland, M. R., Elhadad, N. & Pratt, W. Informatics for sex- and gender-related health: understanding the problems, developing new methods, and designing new solutions. J. Am. Med. Inform. Assoc. 29, 225\u2013229 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Park, Y. et al. Comparison of methods to reduce bias from clinical prediction models of postpartum depression. JAMA Netw. Open 4, e213909 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, W. et al. Global biobank meta-analysis initiative: powering genetic discovery across human disease. Cell Genom. 2, 100192 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jian, X. et al. Genome-wide association study of cognitive function in diverse Hispanics\/Latinos: results from the Hispanic Community Health Study\/Study of Latinos. Transl. Psychiatry 10, 245 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Migliozzi, S. et al. Integrative multi-omics networks identify PKC\u03b4 and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy. Nat. Cancer 4, 181\u2013202 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bravo Gonz\u00e1lez-Blas, C. et al. Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation. Nat. Cell Biol. 26, 153\u2013167 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fatima, I. et al. Breakthroughs in AI and multi-omics for cancer drug discovery: a review. Eur. J. Med. Chem. 280, 116925 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wen, Z. et al. Genetic insights into idiopathic pulmonary fibrosis: a multi-omics approach to identify potential therapeutic targets. J. Transl. Med. 23, 337 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gao, R. et al. Secreted MUP1 that reduced under ER stress attenuates ER stress induced insulin resistance through suppressing protein synthesis in hepatocytes. Pharmacol. Res. 187, 106585 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, T. et al. Identification of serum biomarkers and therapeutic targets for aortic diseases in obesity through multi-omics analysis. J. Thorac. Dis. 16, 8435\u20138449 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, Y. X. et al. An integrative multi-omics network-based approach identifies key regulators for breast cancer. Comput. Struct. Biotechnol. J. 18, 2826\u20132835 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kim, N. et al. CCR8 as a therapeutic novel target: omics-integrated comprehensive analysis for systematically prioritizing indications. Biomedicines 11, 2910 (2023).<\/p>\n

Vos, W. et al. The 2000HIV study: design, multi-omics methods and participant characteristics. Front. Immunol. 13, 982746 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, B. et al. NetSDR: Drug repurposing for cancers based on subtype-specific network modularization and perturbation analysis. Biochim. Biophys. Acta Mol. Basis Dis. 1871, 167688 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zillich, E. et al. Multi-omics profiling of DNA methylation and gene expression alterations in human cocaine use disorder. Transl. Psychiatry 14, 428 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Karaosmanoglu, K., Sayar, N. A., Kurnaz, I. A. & Akbulut, B. S. Assessment of berberine as a multi-target antimicrobial: a multi-omics study for drug discovery and repositioning. OMICS 18, 42\u201353 (2014).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Krumm, J. et al. High-throughput screening and proteomic characterization of compounds targeting myeloid-derived suppressor cells. Mol. Cell Proteom. 22, 100632 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hell, T. et al. Combining activity profiling with advanced annotation to accelerate the discovery of natural products targeting oncogenic signaling in melanoma. J. Nat. Prod. 85, 1540\u20131554 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ory, L. et al. Targeting bioactive compounds in natural extracts\u2014development of a comprehensive workflow combining chemical and biological data. Anal. Chim. Acta 1070, 29\u201342 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhu, J. et al. SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction. BMC Bioinforma. 25, 39 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, Z. & Wei, Z. A framework for pre-training biomedical knowledge graphs with graph neural networks. Comput. Biol. Med. 178, 108768 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

He, C. et al. Multi-type feature fusion based on graph neural network for drug-drug interaction prediction. BMC Bioinforma. 23, 224 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, Y. et al. Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning. Methods 179, 37\u201346 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ren, Z. H. et al. BioChemDDI: predicting drug-drug interactions by fusing biochemical and structural information through a self-attention mechanism. Biology 11, 758 (2022).<\/p>\n

Zang, X., Zhao, X. & Tang, B. Hierarchical molecular graph self-supervised learning for property prediction. Commun. Chem. 6, 34 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, Z. et al. DEML: drug synergy and interaction prediction using ensemble-based multi-task learning. Molecules 28, 844 (2023).<\/p>\n

Coaviche-Yoval, A. et al. In silico and in vivo neuropharmacological evaluation of two \u03b3-amino acid isomers derived from 2,3-disubstituted benzofurans, as ligands of GluN1-GluN2A NMDA receptor. Amino Acids 54, 215\u2013228 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tang, Y. et al. Structure-based discovery of CZL80, a caspase-1 inhibitor with therapeutic potential for febrile seizures and later enhanced epileptogenic susceptibility. Br. J. Pharmacol. 177, 3519\u20133534 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Qiang, S. J. et al. The discovery of novel PGK1 activators as apoptotic inhibiting and neuroprotective agents. Front. Pharmacol. 13, 877706 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pandey, P. et al. Structure-based identification of potent natural product chemotypes as cannabinoid receptor 1 inverse agonists. Molecules 23, 2630 (2018).<\/p>\n

Ozhathil, L. C. et al. Identification of potent and selective small molecule inhibitors of the cation channel TRPM4. Br. J. Pharmacol. 175, 2504\u20132519 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kampen, S. et al. Structure-based discovery of negative allosteric modulators of the metabotropic glutamate receptor 5. ACS Chem. Biol. 17, 2744\u20132752 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, J. et al. Machine-learning-enabled virtual screening for inhibitors of lysine-specific histone demethylase 1. Molecules 26, 7492 (2021).<\/p>\n

Wei, L. et al. Hit identification driven by combining artificial intelligence and computational chemistry methods: a PI5P4K-\u03b2 case study. J. Chem. Inf. Model. 63, 5341\u20135355 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Reker, D., Schneider, P. & Schneider, G. Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors. Chem. Sci. 7, 3919\u20133927 (2016).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Boniolo, F. et al. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin. Drug Discov. 16, 991\u20131007 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Limeta, A. et al. Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors. Comput. Struct. Biotechnol. J. 21, 3912\u20133919 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fountzilas, E., Tsimberidou, A. M., Vo, H. H. & Kurzrock, R. Clinical trial design in the era of precision medicine. Genome Med. 14, 101 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Moingeon, P., Kuenemann, M. & Guedj, M. Artificial intelligence-enhanced drug design and development: toward a computational precision medicine. Drug Discov. Today 27, 215\u2013222 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ahmed, Z. Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis. Prog. Mol. Biol. Transl. Sci. 190, 101\u2013125 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kolla, L. et al. The case for AI-driven cancer clinical trials\u2014the efficacy arm in silico. Biochim Biophys. Acta Rev. Cancer 1876, 188572 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hartl, D. et al. Translational precision medicine: an industry perspective. J. Transl. Med. 19, 245 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sedano, R. et al. Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review. Ther. Adv. Gastroenterol. 18, 17562848251321915 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wei, L. et al. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br. J. Radiol. 96, 20230211 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Terranova, N. & Venkatakrishnan, K. Machine learning in modeling disease trajectory and treatment outcomes: an emerging enabler for model-informed precision medicine. Clin. Pharmacol. Ther. 115, 720\u2013726 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lorkowski, J., Kolaszy\u0144ska, O. & Pokorski, M. Artificial intelligence and precision medicine: a perspective. Adv. Exp. Med. Biol. 1375, 1\u201311 (2022).<\/p>\n

PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n","protected":false},"excerpt":{"rendered":"Minikel, E. V., Painter, J. L., Dong, C. C. & Nelson, M. R. Refining the impact of genetic…\n","protected":false},"author":2,"featured_media":64594,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,7044,36542,1720,1941,617,5368,21053,5813,7290],"class_list":["post-64593","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-artificial-intelligence","tag-cancer-research","tag-cell-biology","tag-drug-development","tag-drug-discovery","tag-general","tag-internal-medicine","tag-medicine-public-health","tag-oncology","tag-pathology"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/64593","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=64593"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/64593\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/64594"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=64593"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=64593"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=64593"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}