Target identification and assessment in the era of AI – Nature Reviews Drug Discovery

Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. 9, eaag1166 (2017).

Article  PubMed  PubMed Central  Google Scholar 

Zhou, Y. et al. TTD: therapeutic target database describing target druggability information. Nucleic Acids Res. 52, D1465–D1477 (2024).

Article  PubMed  PubMed Central  Google Scholar 

Acharya, K. R., Sturrock, E. D., Riordan, J. F. & Ehlers, M. R. ACE revisited: a new target for structure-based drug design. Nat. Rev. Drug. Discov. 2, 891–902 (2003).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zaman, M. A., Oparil, S. & Calhoun, D. A. Drugs targeting the renin–angiotensin–aldosterone system. Nat. Rev. Drug. Discov. 1, 621–636 (2002).

Article  CAS  PubMed  Google Scholar 

Slamon, D. J. et al. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235, 177–182 (1987).

Article  CAS  PubMed  Google Scholar 

Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 417, 949–954 (2002).

Article  CAS  PubMed  Google Scholar 

Sabatine, M. S. PCSK9 inhibitors: clinical evidence and implementation. Nat. Rev. Cardiol. 16, 155–165 (2019).

Article  CAS  PubMed  Google Scholar 

Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

Article  CAS  PubMed  Google Scholar 

Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021).

Article  PubMed  PubMed Central  Google Scholar 

McDonagh, E. M. et al. Human genetics and genomics for drug target identification and prioritization: Open Targets’ perspective. Annu. Rev. Biomed. Data Sci. 7, 59–81 (2024).

Article  PubMed  Google Scholar 

Minikel, E. V., Painter, J. L., Dong, C. C. & Nelson, M. R. Refining the impact of genetic evidence on clinical success. Nature 629, 624–629 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug. Discov. 12, 581–594 (2013).

Article  CAS  PubMed  Google Scholar 

Plenge, R. M. Disciplined approach to drug discovery and early development. Sci. Transl. Med. 8, 349ps315 (2016).

Article  Google Scholar 

Michoel, T. & Zhang, J. D. Causal inference in drug discovery and development. Drug. Discov. Today 28, 103737 (2023).

Article  CAS  PubMed  Google Scholar 

Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

Article  CAS  PubMed  Google Scholar 

Bretherick, A. D. et al. Linking protein to phenotype with Mendelian randomization detects 38 proteins with causal roles in human diseases and traits. PLoS Genet. 16, e1008785 (2020).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhao, J. H. et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat. Immunol. 24, 1540–1551 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pavlidis, P. et al. Interleukin-22 regulates neutrophil recruitment in ulcerative colitis and is associated with resistance to ustekinumab therapy. Nat. Commun. 13, 5820 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gonzalez, G. et al. Combinatorial prediction of therapeutic perturbations using causally inspired neural networks. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-025-01481-x (2025).

Article  PubMed  Google Scholar 

Cheng, A. C. et al. Structure-based maximal affinity model predicts small-molecule druggability. Nat. Biotechnol. 25, 71–75 (2007).

Article  PubMed  Google Scholar 

Bennett, C. F. Therapeutic antisense oligonucleotides are coming of age. Annu. Rev. Med. 70, 307–321 (2019).

Article  CAS  PubMed  Google Scholar 

Qin, S. et al. mRNA-based therapeutics: powerful and versatile tools to combat diseases. Signal. Transduct. Target. Ther. 7, 166 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sadelain, M., Riviere, I. & Riddell, S. Therapeutic T cell engineering. Nature 545, 423–431 (2017).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Brennan, R. J. Target safety assessment: strategies and resources. Methods Mol. Biol. 1641, 213–228 (2017).

Article  CAS  PubMed  Google Scholar 

Brennan, R. J. et al. The state of the art in secondary pharmacology and its impact on the safety of new medicines. Nat. Rev. Drug. Discov. https://doi.org/10.1038/s41573-024-00942-3 (2024).

Article  PubMed  Google Scholar 

Simonovsky, M. & Meyers, J. DeeplyTough: learning structural comparison of protein binding sites. J. Chem. Inf. Model. 60, 2356–2366 (2020).

Article  CAS  PubMed  Google Scholar 

Pun, F. W., Ozerov, I. V. & Zhavoronkov, A. AI-powered therapeutic target discovery. Trends Pharmacol. Sci. 44, 561–572 (2023).

Article  CAS  PubMed  Google Scholar 

Spring, L., Demuren, K., Ringel, M. & Wu, J. First-in-class versus best-in-class: an update for new market dynamics. Nat. Rev. Drug. Discov. 22, 531–532 (2023).

Article  CAS  PubMed  Google Scholar 

Long, X. et al. AI-enabled cancer target prioritization with optimal profiles balancing novelty, confidence and commercial tractability. Future Medicine AI https://doi.org/10.2217/fmai-2023-0019 (2024).

Hill, J. A. & Cowen, L. E. Using combination therapy to thwart drug resistance. Future Microbiol. 10, 1719–1726 (2015).

Article  CAS  PubMed  Google Scholar 

Wen, P. Y. et al. Dabrafenib plus trametinib in patients with BRAF^V600E-mutant low-grade and high-grade glioma (ROAR): a multicentre, open-label, single-arm, phase 2, basket trial. Lancet Oncol. 23, 53–64 (2022).

Article  CAS  PubMed  Google Scholar 

Subbiah, V. et al. Dabrafenib plus trametinib in BRAF^V600E-mutated rare cancers: the phase 2 ROAR trial. Nat. Med. 29, 1103–1112 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Choueiri, T. K. et al. Cabozantinib plus nivolumab and ipilimumab in renal-cell carcinoma. N. Engl. J. Med. 388, 1767–1778 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Owonikoko, T. K. et al. Nivolumab and ipilimumab as maintenance therapy in extensive-disease small-cell lung cancer: CheckMate 451. J. Clin. Oncol. 39, 1349–1359 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lenz, H. J. et al. First-line nivolumab plus low-dose ipilimumab for microsatellite instability-high/mismatch repair-deficient metastatic colorectal cancer: the phase II CheckMate 142 study. J. Clin. Oncol. 40, 161–170 (2022).

Article  CAS  PubMed  Google Scholar 

Barry, M., Mulcahy, F. & Back, D. J. Antiretroviral therapy for patients with HIV disease. Br. J. Clin. Pharmacol. 45, 221–228 (1998).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lathouwers, E. et al. Week 48 resistance analyses of the once-daily, single-tablet regimen darunavir/cobicistat/emtricitabine/tenofovir alafenamide (D/C/F/TAF) in adults living with HIV-1 from the phase III randomized AMBER and EMERALD trials. AIDS Res. Hum. Retroviruses 36, 48–57 (2020).

Article  CAS  PubMed  Google Scholar 

Powles, T. et al. Enfortumab vedotin and pembrolizumab in untreated advanced urothelial cancer. N. Engl. J. Med. 390, 875–888 (2024).

Article  CAS  PubMed  Google Scholar 

Hoimes, C. J. et al. Enfortumab vedotin plus pembrolizumab in previously untreated advanced urothelial cancer. J. Clin. Oncol. 41, 22–31 (2023).

Article  CAS  PubMed  Google Scholar 

Eadie, A. L., Brunt, K. R. & Herder, M. Exploring the Food and Drug Administration’s review and approval of Entresto (sacubitril/valsartan). Pharmacol. Res. Perspect. 9, e00794 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Mann, D. L. et al. Effect of treatment with sacubitril/valsartan in patients with advanced heart failure and reduced ejection fraction: a randomized clinical trial. JAMA Cardiol. 7, 17–25 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Vaduganathan, M. et al. Sacubitril/valsartan in heart failure with mildly reduced or preserved ejection fraction: a pre-specified participant-level pooled analysis of PARAGLIDE-HF and PARAGON-HF. Eur. Heart J. 44, 2982–2993 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zeng, Z. et al. Deep learning for cancer type classification and driver gene identification. BMC Bioinforma. 22, 491 (2021).

Article  CAS  Google Scholar 

Kim, D. et al. An evolution-based machine learning to identify cancer type-specific driver mutations. Brief. Bioinform 24, bbac593 (2023).

Article  PubMed  Google Scholar 

Chakraborty, S., Hosen, M. I., Ahmed, M. & Shekhar, H. U. Onco-multi-OMICS approach: a new frontier in cancer research. Biomed. Res. Int. 2018, 9836256 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Schulte-Sasse, R., Budach, S., Hnisz, D. & Marsico, A. Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nat. Mach. Intell. 3, 513–526 (2021).

Article  Google Scholar 

Sidorenko, D. et al. Precious2GPT: the combination of multiomics pretrained transformer and conditional diffusion for artificial multi-omics multi-species multi-tissue sample generation. npj Aging 10, 37 (2024).

Article  PubMed  PubMed Central  Google Scholar 

Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21, 1470–1480 (2024).

Article  CAS  PubMed  Google Scholar 

Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D. & Carpenter, A. E. Image-based profiling for drug discovery: due for a machine-learning upgrade? Nat. Rev. Drug. Discov. 20, 145–159 (2021).

Article  CAS  PubMed  Google Scholar 

Phillip, J. M., Han, K.-S., Chen, W.-C., Wirtz, D. & Wu, P.-H. A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei. Nat. Protoc. 16, 754–774 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Xue, Y., Wang, J., Ren, K. & Ji, J. Deep mining of subtle differences in cell morphology via deep learning. Adv. Theory Simul. 4, 2000172 (2021).

Article  Google Scholar 

Chandrasekaran, S. N. et al. JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations. Preprint at bioRxiv https://doi.org/10.1101/2023.03.23.534023 (2023).

Rawat, W. & Wang, Z. Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29, 2352–2449 (2017).

Article  PubMed  Google Scholar 

Ektefaie, Y., Dasoulas, G., Noori, A., Farhat, M. & Zitnik, M. Multimodal learning with graphs. Nat. Mach. Intell. 5, 340–350 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Yu, S. et al. Integrating inflammatory biomarker analysis and artificial-intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis. Cell Chem. Biol. 30, 1169–1182.e8 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Atmaramani, R. et al. Deep learning analysis on images of iPSC-derived motor neurons carrying fALS-genetics reveals disease-relevant phenotypes. Preprint at bioRxiv https://doi.org/10.1101/2024.01.04.574270 (2024).

Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Oughtred, R. et al. The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 30, 187–200 (2021).

Article  CAS  PubMed  Google Scholar 

Del Toro, N. et al. The IntAct database: efficient access to fine-grained molecular interaction data. Nucleic Acids Res. 50, D648–D653 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Bader, G. D., Betel, D. & Hogue, C. W. BIND: the biomolecular interaction network database. Nucleic Acids Res. 31, 248–250 (2003).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Milacic, M. et al. The Reactome pathway knowledgebase 2024. Nucleic Acids Res. 52, D672–D678 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pico, A. R. et al. WikiPathways: pathway editing for the people. PLoS Biol. 6, e184 (2008).

Article  PubMed  PubMed Central  Google Scholar 

Gene Ontology, C. et al. The Gene Ontology knowledgebase in 2023. Genetics 224, iyad031 (2023).

Article  Google Scholar 

Zhang, Y. et al. A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research. Nat. Mach. Intell. 7, 602–614 (2025).

Article  Google Scholar 

Karki, R. et al. KGG: a fully automated workflow for creating disease-specific knowledge graphs. Bioinformatics 41, btaf383 (2025).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chandak, P., Huang, K. & Zitnik, M. Building a knowledge graph to enable precision medicine. Sci. Data 10, 67 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Dezso, Z. & Ceccarelli, M. Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinforma. 21, 104 (2020).

Article  CAS  Google Scholar 

Zhao, W., Gu, X., Chen, S., Wu, J. & Zhou, Z. MODIG: integrating multi-omics and multi-dimensional gene network for cancer driver gene identification based on graph attention network model. Bioinformatics 38, 4901–4907 (2022).

Article  CAS  PubMed  Google Scholar 

Kamya, P. et al. PandaOmics: an AI-driven platform for therapeutic target and biomarker discovery. J. Chem. Inf. Model. 64, 3961–3969 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Paliwal, S., de Giorgio, A., Neil, D., Michel, J. B. & Lacoste, A. M. Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs. Sci. Rep. 10, 18250 (2020).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wang, S. et al. KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers. Bioinformatics 37, i418–i425 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Oliveira, A. L. Biotechnology, big data and artificial intelligence. Biotechnol. J. 14, e1800613 (2019).

Article  PubMed  Google Scholar 

Chia, S. et al. Phenotype-driven precision oncology as a guide for clinical decisions one patient at a time. Nat. Commun. 8, 435 (2017).

Article  PubMed  PubMed Central  Google Scholar 

Bajwa, J., Munir, U., Nori, A. & Williams, B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc. J. 8, e188–e194 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Gao, Z. et al. Artificial intelligence-based drug repurposing with electronic health record clinical corroboration: a case for ketamine as a potential treatment for amphetamine-type stimulant use disorder. Addiction 120, 732–744 (2025).

Article  PubMed  Google Scholar 

Liu, R., Wei, L. & Zhang, P. A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. Nat. Mach. Intell. 3, 68–75 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Morselli Gysi, D. et al. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc. Natl Acad. Sci. USA 118, e2025581118 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Serrano Nájera, G., Narganes Carlón, D. & Crowther, D. J. TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery. Sci. Rep. 11, 15747 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Lee, B. Y., Bacon, K. M., Bottazzi, M. E. & Hotez, P. J. Global economic burden of Chagas disease: a computational simulation model. Lancet Infect. Dis. 13, 342–348 (2013).

Article  PubMed  PubMed Central  Google Scholar 

Senger, S. Assessment of the significance of patent-derived information for the early identification of compound–target interaction hypotheses. J. Cheminform 9, 26 (2017).

Article  PubMed  PubMed Central  Google Scholar 

van Rijn, T. & Timmis, J. K. Patent landscape analysis—contributing to the identification of technology trends and informing research and innovation funding policy. Microb. Biotechnol. 16, 683–696 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Kesselheim, A. S., Cook-Deegan, R. M., Winickoff, D. E. & Mello, M. M. Gene patenting—the supreme court finally speaks. N. Engl. J. Med. 369, 869–875 (2013).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Daluwatte, C., Schotland, P., Strauss, D. G., Burkhart, K. K. & Racz, R. Predicting potential adverse events using safety data from marketed drugs. BMC Bioinforma. 21, 163 (2020).

Article  Google Scholar 

Sterzi, V. Patent quality and ownership: an analysis of UK faculty patenting. Res. Policy 42, 564–576 (2013).

Article  Google Scholar 

Ciray, F. & Dogan, T. Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features. Expert. Opin. Drug. Discov. 17, 1425–1441 (2022).

Article  CAS  PubMed  Google Scholar 

Ekins, S., Mestres, J. & Testa, B. In silico pharmacology for drug discovery: applications to targets and beyond. Br. J. Pharmacol. 152, 21–37 (2007).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sudhahar, S. et al. An experimentally validated approach to automated biological evidence generation in drug discovery using knowledge graphs. Nat. Commun. 15, 5703 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Piñero, J. et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45, D833–D839 (2017).

Article  PubMed  Google Scholar 

Chen, Y. A., Allendes Osorio, R. S. & Mizuguchi, K. TargetMine 2022: a new vision into drug target analysis. Bioinformatics 38, 4454–4456 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen, Y. A., Tripathi, L. P. & Mizuguchi, K. TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery. PLoS One 6, e17844 (2011).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Madhukar, N. S. et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat. Commun. 10, 5221 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Allen, J. E. et al. Dual inactivation of Akt and ERK by TIC10 signals Foxo3a nuclear translocation, TRAIL gene induction, and potent antitumor effects. Sci. Transl. Med. 5, 171ra117 (2013).

Article  Google Scholar 

Mountjoy, E. et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat. Genet. 53, 1527–1533 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Abedi, M. et al. Systems biology and machine learning approaches identify drug targets in diabetic nephropathy. Sci. Rep. 11, 23452 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Binder, J. et al. Machine learning prediction and tau-based screening identifies potential Alzheimer’s disease genes relevant to immunity. Commun. Biol. 5, 125 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Buniello, A. et al. Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Res. 53, D1467–D1475 (2025).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Leung, H. et al. Advancing target discovery through disease-specific integration of multi-modal target identification models and comprehensive benchmarking system. Sci. Rep. https://doi.org/10.1038/s41598-026-47765-3 (2026).

Choobdar, S. et al. Assessment of network module identification across complex diseases. Nat. Methods 16, 843–852 (2019).

Article  CAS  PubMed  Google Scholar 

Zeng, X. et al. Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework. Nat. Mach. Intell. 4, 1004–1016 (2022).

Article  Google Scholar 

Ling, C. et al. Predicting drug–target interactions using matrix factorization with self-paced learning and dual similarity information. Technol. Health Care 32, 49–64 (2024).

Article  PubMed  PubMed Central  Google Scholar 

Liu, Y., Wu, M., Miao, C., Zhao, P. & Li, X. L. Neighborhood regularized logistic matrix factorization for drug–target interaction prediction. PLoS Comput. Biol. 12, e1004760 (2016).

Article  PubMed  PubMed Central  Google Scholar 

Raies, A. et al. DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets. Commun. Biol. 5, 1291 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Muslu, O., Hoyt, C. T., Lacerda, M., Hofmann-Apitius, M. & Frohlich, H. GuiltyTargets: prioritization of novel therapeutic targets with network representation learning. IEEE/ACM Trans. Comput. Biol. Bioinform 19, 491–500 (2022).

Article  CAS  PubMed  Google Scholar 

Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kraus, O. et al. Masked autoencoders are scalable learners of cellular morphology. Preprint at 10.48550/arXiv.2309.16064 (2023).

Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. In Proc. 35th Int. Conf. Neural Information Processing Systems (eds Ranzato, M. et al.) 29287–29303 (Curran Associates, 2021).

Ngoi, N. Y. L. et al. Synthetic lethal strategies for the development of cancer therapeutics. Nat. Rev. Clin. Oncol. 22, 46–64 (2025).

Article  PubMed  Google Scholar 

He, R., Cao, J. & Tan, T. Generative artificial intelligence: a historical perspective. Natl Sci. Rev. 12, nwaf050 (2025).

Article  PubMed  PubMed Central  Google Scholar 

Urban, A. et al. Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery. Aging 15, 4649–4666 (2023).

CAS  PubMed  PubMed Central  Google Scholar 

Galkin, F. et al. Precious3GPT: multimodal multi-species multi-omics multi-tissue transformer for aging research and drug discovery. Preprint at bioRxiv https://doi.org/10.1101/2024.07.25.605062 (2024).

Galkin, F., Ren, F. & Zhavoronkov, A. LLMs and AI life models for Traditional Chinese Medicine-derived geroprotector formulation. Aging Dis. https://doi.org/10.14336/ad.2024.1697 (2025).

Article  PubMed  PubMed Central  Google Scholar 

Zappia, L., Phipson, B. & Oshlack, A. Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 18, 174 (2017).

Article  PubMed  PubMed Central  Google Scholar 

Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11, 166 (2020).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lindenbaum, O., Stanley, J., Wolf, G. & Krishnaswamy, S. Geometry based data generation. In Advances in Neural Information Processing Systems Vol. 31, 1405–1416 (NeurIPS, 2018).

Zinati, Y., Takiddeen, A. & Emad, A. GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks. Nat. Commun. 15, 4055 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lotfollahi, M., Wolf, F. A. & Theis, F. J. scGen predicts single-cell perturbation responses. Nat. Methods 16, 715–721 (2019).

Article  CAS  PubMed  Google Scholar 

Achiam, J. et al. GPT-4 technical report. Preprint at https://doi.org/10.48550/arXiv.2303.08774 (2023).

Gururangan, S. et al. Don’t stop pretraining: adapt language models to domains and tasks. In Proc. 58th Annual Meeting of the Association for Computational Linguistics (eds Jurafsky, D. et al.) 8342–8360 (Association for Computational Linguistics, 2020).

Qiu, X. et al. Pre-trained models for natural language processing: a survey. Sci. China Technol. Sci. 63, 1872–1897 (2020).

Article  Google Scholar 

Luo, R. et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinform 23, bbac409 (2022).

Article  PubMed  Google Scholar 

Zagirova, D. et al. Biomedical generative pre-trained based transformer language model for age-related disease target discovery. Aging 15, 9293–9309 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wang, E. et al. Txgemma: efficient and agentic LLMs for therapeutics. Preprint at https://arxiv.org/abs/2504.06196 (2025).

Gottweis, J. et al. Towards an AI co-scientist. Preprint at https://doi.org/10.48550/arXiv.2502.18864 (2025).

Guan, Y. et al. AI-Assisted drug re-purposing for human liver fibrosis. Adv. Sci. 12, e08751 (2025).

Article  Google Scholar 

Zhang, Z. et al. OriGene: a self-evolving virtual disease biologist automating therapeutic target discovery. Preprint at bioRxiv https://doi.org/10.1101/2025.06.03.657658 (2025).

Pun, F. W. et al. Identification of therapeutic targets for amyotrophic lateral sclerosis using PandaOmics—an AI-enabled biological target discovery platform. Front. Aging Neurosci. 14, 914017 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ren, F. et al. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02143-0 (2024).

Article  PubMed  PubMed Central  Google Scholar 

Lim, C. M. et al. Multiomic prediction of therapeutic targets for human diseases associated with protein phase separation. Proc. Natl Acad. Sci. USA 120, e2300215120 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mkrtchyan, G. V. et al. High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders. Cell Death Dis. 13, 999 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pun, F. W. et al. A comprehensive AI-driven analysis of large-scale omic datasets reveals novel dual-purpose targets for the treatment of cancer and aging. Aging Cell 22, e14017 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ranjbar, M. et al. Autophagy dark genes: can we find them with machine learning? Nat. Sci. 3, e20220067 (2023).

Article  CAS  Google Scholar 

Hughes, J. P., Rees, S., Kalindjian, S. B. & Philpott, K. L. Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249 (2011).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ren, F. et al. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor. Chem. Sci. 14, 1443–1452 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang, S. et al. Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis. Neuron 110, 992–1008.e11 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liu, B. H. M. et al. Utilizing AI for the identification and validation of novel therapeutic targets and repurposed drugs for endometriosis. Adv. Sci. 12, e2406565 (2025).

Article  Google Scholar 

Tang, Q. et al. AI-driven robotics laboratory identifies pharmacological TNIK inhibition as a potent senomorphic agent. Aging Dis. https://doi.org/10.14336/ad.2024.1492 (2025).

Article  PubMed  PubMed Central  Google Scholar 

Tsuji, S. et al. Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease. Alzheimers Res. Ther. 13, 92 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Yamaguchi, T. et al. Syk inhibitors reduce tau protein phosphorylation and oligomerization. Neurobiol. Dis. 201, 106656 (2024).

Article  CAS  PubMed  Google Scholar 

Pun, F. W. et al. Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine. Aging 14, 2475–2506 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sikder, S., Baek, S., McNeil, T. & Dalal, Y. Centromere inactivation during aging can be rescued in human cells. Mol. Cell 85, 692–707.e7 (2025).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Deng, X. et al. Effects of MMP2 and its inhibitor TIMP2 on DNA damage, apoptosis and senescence of human lens epithelial cells induced by oxidative stress. J. Bioenerg. Biomembr. 56, 619–630 (2024).

Article  CAS  PubMed  Google Scholar 

Zhu, F. et al. What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical, and systems profiles of successful targets. J. Pharmacol. Exp. Ther. 330, 304–315 (2009).

Article  CAS  PubMed  Google Scholar 

Zhu, F., Li, X. X., Yang, S. Y. & Chen, Y. Z. Clinical success of drug targets prospectively predicted by in silico study. Trends Pharmacol. Sci. 39, 229–231 (2018).

Article  CAS  PubMed  Google Scholar 

Aliper, A. et al. Prediction of clinical trials outcomes based on target choice and clinical trial design with multi-modal artificial intelligence. Clin. Pharmacol. Ther. 114, 972–980 (2023).

Article  PubMed  Google Scholar 

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fraser, J. S. et al. Accessing protein conformational ensembles using room-temperature X-ray crystallography. Proc. Natl Acad. Sci. USA 108, 16247–16252 (2011).

Article  CAS  PubMed  PubMed Central  Google Scholar 

ww, P. D. B. c Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 47, D520–D528 (2019).

Article  Google Scholar 

Steinegger, M., Mirdita, M. & Soding, J. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nat. Methods 16, 603–606 (2019).

Article  CAS  PubMed  Google Scholar 

Jumper, J. et al. Applying and improving AlphaFold at CASP14. Proteins 89, 1711–1721 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lee, C., Su, B. H. & Tseng, Y. J. Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors. Brief. Bioinform 23, bbac308 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Webb, B. & Sali, A. Protein structure modeling with MODELLER. Methods Mol. Biol. 1137, 1–15 (2014).

Article  CAS  PubMed  Google Scholar 

Stein, A. & Kortemme, T. Improvements to robotics-inspired conformational sampling in Rosetta. PLoS One 8, e63090 (2013).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Deane, C. M. & Blundell, T. L. CODA: a combined algorithm for predicting the structurally variable regions of protein models. Protein Sci. 10, 599–612 (2001).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ahdritz, G. et al. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nat. Methods 21, 1514–1524 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Passaro, S. et al. Boltz-2: towards accurate and efficient binding affinity prediction. Preprint at bioRxiv https://doi.org/10.1101/2025.06.14.659707 (2025).

Naddaf, M. Open-source protein structure AI aims to match AlphaFold. Nature https://doi.org/10.1038/d41586-025-03546-y (2025).

Article  PubMed  Google Scholar 

Mosalaganti, S. et al. AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science 376, eabm9506 (2022).

Article  CAS  PubMed  Google Scholar 

Bryant, P., Pozzati, G. & Elofsson, A. Improved prediction of protein–protein interactions using AlphaFold2. Nat. Commun. 13, 1265 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hekkelman, M. L., de Vries, I., Joosten, R. P. & Perrakis, A. AlphaFill: enriching AlphaFold models with ligands and cofactors. Nat. Methods 20, 205–213 (2023).

Article  CAS  PubMed  Google Scholar 

Holcomb, M., Chang, Y. T., Goodsell, D. S. & Forli, S. Evaluation of AlphaFold2 structures as docking targets. Protein Sci. 32, e4530 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Scardino, V., Di Filippo, J. I. & Cavasotto, C. N. How good are AlphaFold models for docking-based virtual screening? iScience 26, 105920 (2023).

Article  CAS  PubMed  Google Scholar 

Karelina, M., Noh, J. J. & Dror, R. O. How accurately can one predict drug binding modes using AlphaFold models? eLife 12, RP89386 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lyu, J. et al. AlphaFold2 structures guide prospective ligand discovery. Science 384, eadn6354 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

Article  CAS  PubMed  Google Scholar 

Meller, A., Bhakat, S., Solieva, S. & Bowman, G. R. Accelerating cryptic pocket discovery using AlphaFold. J. Chem. Theory Comput. 19, 4355–4363 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Meller, A. et al. Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network. Nat. Commun. 14, 1177 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li, H. et al. Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning. BMC Rheumatol. 7, 44 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Pham, T. C. P. et al. TNIK is a conserved regulator of glucose and lipid metabolism in obesity. Sci. Adv. 9, eadf7119 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Xu, Z. et al. A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nat. Med. https://doi.org/10.1038/s41591-025-03743-2 (2025).

Article  PubMed  PubMed Central  Google Scholar 

Zitnik, M. AI-enabled drug discovery reaches clinical milestone. Nat. Med. 31, 2490–2491 (2025).

Article  CAS  PubMed  Google Scholar 

Besse-Patin, A. et al. Effect of endurance training on skeletal muscle myokine expression in obese men: identification of apelin as a novel myokine. Int. J. Obes. 38, 707–713 (2014).

Article  CAS  Google Scholar 

Fujie, S. et al. Reduction of arterial stiffness by exercise training is associated with increasing plasma apelin level in middle-aged and older adults. PLoS One 9, e93545 (2014).

Article  PubMed  PubMed Central  Google Scholar 

Vinel, C. et al. The exerkine apelin reverses age-associated sarcopenia. Nat. Med. 24, 1360–1371 (2018).

Article  CAS  PubMed  Google Scholar 

Wang, Y. et al. Oral-096 The apelin receptor agonist azelaprag reduces weight gain & improves body composition in DIO mice. Obesity 32, 5–54 (2024).

Google Scholar 

Mejzini, R. et al. ALS genetics, mechanisms, and therapeutics: where are we now? Front. Neurosci. 13, 1310 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Ilieva, H., Vullaganti, M. & Kwan, J. Advances in molecular pathology, diagnosis, and treatment of amyotrophic lateral sclerosis. BMJ 383, e075037 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Shi, Y. et al. Haploinsufficiency leads to neurodegeneration in C9ORF72 ALS/FTD human induced motor neurons. Nat. Med. 24, 313–325 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hung, S. T. et al. PIKFYVE inhibition mitigates disease in models of diverse forms of ALS. Cell 186, 786–802.e28 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Terstappen, G. C., Schlüpen, C., Raggiaschi, R. & Gaviraghi, G. Target deconvolution strategies in drug discovery. Nat. Rev. Drug. Discov. 6, 891–903 (2007).

Article  CAS  PubMed  Google Scholar 

Wang, X. et al. A novel approach for target deconvolution from phenotype-based screening using knowledge graph. Sci. Rep. 15, 2414 (2025).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Allen, J. E. et al. Discovery and clinical introduction of first-in-class imipridone ONC201. Oncotarget 7, 74380–74392 (2016).

Article  PubMed  PubMed Central  Google Scholar 

Anderson, P. M. et al. Phase II study of ONC201 in neuroendocrine tumors including pheochromocytoma–paraganglioma and desmoplastic small round cell tumor. Clin. Cancer Res. 28, 1773–1782 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ishizawa, J. et al. Mitochondrial ClpP-mediated proteolysis induces selective cancer cell lethality. Cancer Cell 35, 721–737.e9 (2019).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Graves, P. R. et al. Mitochondrial protease ClpP is a target for the anticancer compounds ONC201 and related analogues. ACS Chem. Biol. 14, 1020–1029 (2019).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Price, E. et al. What is the real value of omics data? Enhancing research outcomes and securing long-term data excellence. Nucleic Acids Res. 52, 12130–12140 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bottini, S., Emmert-Streib, F. & Franco, L. Editorial: AI and multi-omics for rare diseases: challenges, advances and perspectives, volume II. Front. Mol. Biosci. 9, 986749 (2022).

Article  PubMed  PubMed Central  Google Scholar 

All of Us Research Program Genomics Investigators Genomic data in the All of Us Research Program. Nature 627, 340–346 (2024).

Article  Google Scholar 

O’Doherty, K. C. et al. Toward better governance of human genomic data. Nat. Genet. 53, 2–8 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Sun, K. Y. et al. A deep catalogue of protein-coding variation in 983,578 individuals. Nature 631, 583–592 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Errington, T. M. et al. Investigating the replicability of preclinical cancer biology. eLife 10, e71601 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rodgers, P. & Collings, A. What have we learned? eLife https://doi.org/10.7554/eLife.75830 (2021).

Lu, K., Yang, G. & Wang, X. Topics emerged in the biomedical field and their characteristics. Technol. Forecast. Soc. Change 174, 121218 (2022).

Article  Google Scholar 

Krawczyk, B. Learning from imbalanced data: open challenges and future directions. Prog. Artif. Intell. 5, 221–232 (2016).

Article  Google Scholar 

Oprea, T. I. et al. Unexplored therapeutic opportunities in the human genome. Nat. Rev. Drug. Discov. 17, 317–332 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Picard, M., Scott-Boyer, M. P., Bodein, A., Périn, O. & Droit, A. Integration strategies of multi-omics data for machine learning analysis. Comput. Struct. Biotechnol. J. 19, 3735–3746 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Adossa, N., Khan, S., Rytkönen, K. T. & Elo, L. L. Computational strategies for single-cell multi-omics integration. Comput. Struct. Biotechnol. J. 19, 2588–2596 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nat. Med. 28, 1773–1784 (2022).

Article  CAS  PubMed  Google Scholar 

Chen, T., Philip, M., Le Cao, K. A. & Tyagi, S. A multi-modal data harmonisation approach for discovery of COVID-19 drug targets. Brief. Bioinform 22, bbab185 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Chaudhary, K., Poirion, O. B., Lu, L. & Garmire, L. X. Deep Learning-based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 24, 1248–1259 (2018).

Article  CAS  PubMed  Google Scholar 

Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Elbadawi, M., Gaisford, S. & Basit, A. W. Advanced machine-learning techniques in drug discovery. Drug. Discov. Today 26, 769–777 (2021).

Article  CAS  PubMed  Google Scholar 

Castelvecchi, D. Can we open the black box of AI? Nature 538, 20–23 (2016).

Article  CAS  PubMed  Google Scholar 

Hie, B., Cho, H. & Berger, B. Realizing private and practical pharmacological collaboration. Science 362, 347–350 (2018).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jiménez-Luna, J., Grisoni, F. & Schneider, G. Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2, 573–584 (2020).

Article  Google Scholar 

Patel, R. et al. Retrieve to explain: evidence-driven predictions for explainable drug target identification. In Proc. 63rd Annual Meeting of the Association for Computational Linguistics Vol. 1 (eds Che, W. et al.) 3328–3370 (Association for Computational Linguistics, 2025).

Elmarakeby, H. A. et al. Biologically informed deep neural network for prostate cancer discovery. Nature 598, 348–352 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zong, N. et al. BETA: a comprehensive benchmark for computational drug-target prediction. Brief. Bioinform 23, bbac199 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Shayakhmetov, R. et al. Molecular generation for desired transcriptome changes with adversarial autoencoders. Front. Pharmacol. 11, 269 (2020).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vinas, R., Andres-Terre, H., Lio, P. & Bryson, K. Adversarial generation of gene expression data. Bioinformatics 38, 730–737 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dolezal, J. M. et al. Deep learning generates synthetic cancer histology for explainability and education. npj Precis. Oncol. 7, 49 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Giuffrè, M. & Shung, D. L. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. npj Digit. Med. 6, 186 (2023).

Article  PubMed  PubMed Central  Google Scholar 

San, O. The digital twin revolution. Nat. Comput. Sci. 1, 307–308 (2021).

Article  PubMed  Google Scholar 

Ren, Y., Pieper, A.A., Cummings, J.L., Cheng, F. Single-cell digital twins identify drug targets and repurposable medicine in Alzheimer’s disease. Alzheimers Dement 21, e102982 (2025).

Article  PubMed Central  Google Scholar