{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T06:29:07Z","timestamp":1793946547169,"version":"4.8.0"},"reference-count":113,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Critical Reviews in Oncology\/Hematology"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.critrevonc.2026.105461","type":"journal-article","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:14:26Z","timestamp":1782573266000},"page":"105461","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Artificial Intelligence in drug discovery and development: current landscape, challenges, and future perspectives"],"prefix":"10.1016","volume":"226","author":[{"given":"Attila A.","family":"Seyhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudio","family":"Carini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.critrevonc.2026.105461_bib1","doi-asserted-by":"crossref","first-page":"5685","DOI":"10.2147\/DDDT.S509769","article-title":"Artificial intelligence-driven innovations in oncology drug discovery: transforming traditional pipelines and enhancing drug design","volume":"19","author":"Albani","year":"2025","journal-title":"Drug. Des. Devel Ther."},{"issue":"9","key":"10.1016\/j.critrevonc.2026.105461_bib2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0288053","article-title":"Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches","volume":"18","author":"Ashraf","year":"2023","journal-title":"PLoS. One"},{"issue":"3","key":"10.1016\/j.critrevonc.2026.105461_bib3","doi-asserted-by":"crossref","DOI":"10.1002\/wcms.1581","article-title":"Application advances of deep learning methods for de novo drug design and molecular dynamics simulation","volume":"12","author":"Bai","year":"2022","journal-title":"WIREs Comput. Mol. Sci."},{"issue":"W1","key":"10.1016\/j.critrevonc.2026.105461_bib4","doi-asserted-by":"crossref","first-page":"W257","DOI":"10.1093\/nar\/gky318","article-title":"ProTox-II: a webserver for the prediction of toxicity of chemicals","volume":"46","author":"Banerjee","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.critrevonc.2026.105461_bib5","doi-asserted-by":"crossref","first-page":"7493","DOI":"10.2147\/DDDT.S538269","article-title":"Transformative role of artificial intelligence in drug discovery and translational medicine: innovations, challenges, and future prospects","volume":"19","author":"Bassey","year":"2025","journal-title":"Drug. Des. Devel Ther."},{"issue":"6","key":"10.1016\/j.critrevonc.2026.105461_bib6","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1021\/acs.jcim.1c00166","article-title":"Quantum machine learning algorithms for drug discovery applications","volume":"61","author":"Batra","year":"2021","journal-title":"J. Chem. Inf. Model."},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib7","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.drudis.2020.12.009","article-title":"Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet","volume":"26","author":"Bender","year":"2021","journal-title":"Drug. Discov. Today"},{"key":"10.1016\/j.critrevonc.2026.105461_bib8","first-page":"1","article-title":"Atomically accurate de novo design of antibodies with RFdiffusion","author":"Bennett","year":"2025","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The Protein Data Bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res."},{"issue":"6","key":"10.1016\/j.critrevonc.2026.105461_bib10","article-title":"The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies","volume":"16","author":"Blanco-Gonz\u00e1lez","year":"2023","journal-title":"Pharm. (Basel)"},{"key":"10.1016\/j.critrevonc.2026.105461_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.sbi.2022.102526","article-title":"AlphaFold2 protein structure prediction: Implications for drug discovery","volume":"78","author":"Borkakoti","year":"2023","journal-title":"Curr. Opin. Struct. Biol."},{"issue":"12","key":"10.1016\/j.critrevonc.2026.105461_bib12","doi-asserted-by":"crossref","DOI":"10.3390\/genes10120978","article-title":"Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?","volume":"10","author":"Brasil","year":"2019","journal-title":"Genes. (Basel)"},{"issue":"8093","key":"10.1016\/j.critrevonc.2026.105461_bib13","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1038\/d41586-025-03886-9","article-title":"AlphaFold is five years old - these charts show how it revolutionized science","volume":"648","author":"Callaway","year":"2025","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib14","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1186\/s12967-024-05067-0","article-title":"Tribulations and future opportunities for artificial intelligence in precision medicine","volume":"22","author":"Carini","year":"2024","journal-title":"J. Transl. Med."},{"issue":"10","key":"10.1016\/j.critrevonc.2026.105461_bib15","doi-asserted-by":"crossref","first-page":"6340","DOI":"10.1097\/MS9.0000000000002438","article-title":"Generative AI in drug discovery and development: the next revolution of drug discovery and development would be directed by generative AI","volume":"86","author":"Chakraborty","year":"2024","journal-title":"Ann. Med. Surg. (Lond. )"},{"issue":"6","key":"10.1016\/j.critrevonc.2026.105461_bib16","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","article-title":"The rise of deep learning in drug discovery","volume":"23","author":"Chen","year":"2018","journal-title":"Drug. Discov. Today"},{"issue":"6","key":"10.1016\/j.critrevonc.2026.105461_bib17","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","article-title":"The rise of deep learning in drug discovery","volume":"23","author":"Chen","year":"2018","journal-title":"Drug. Discov. Today"},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib18","first-page":"14","article-title":"Introduction to Machine Learning, Neural Networks, and Deep Learning","volume":"9","author":"Choi","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"10.1016\/j.critrevonc.2026.105461_bib19","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.3389\/frhem.2024.1305741","article-title":"De novo drug design through artificial intelligence: an introduction","volume":"3","author":"Crucitti","year":"2024","journal-title":"Front. Hematol."},{"issue":"9","key":"10.1016\/j.critrevonc.2026.105461_bib20","doi-asserted-by":"crossref","DOI":"10.3390\/molecules23092384","article-title":"Deep Learning in Drug Discovery and Medicine; Scratching the Surface","volume":"23","author":"Dana","year":"2018","journal-title":"Molecules"},{"issue":"3","key":"10.1016\/j.critrevonc.2026.105461_bib21","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1007\/s10462-021-10058-4","article-title":"Machine Learning in Drug Discovery: A Review","volume":"55","author":"Dara","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.critrevonc.2026.105461_bib22","article-title":"Artificial intelligence in healthcare: transforming patient safety with intelligent systems-A systematic review","volume":"11","author":"De Micco","year":"2024","journal-title":"Front. Med. (Lausanne)"},{"issue":"7","key":"10.1016\/j.critrevonc.2026.105461_bib23","article-title":"From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes","volume":"18","author":"Dermawan","year":"2025","journal-title":"Pharm. (Basel)"},{"issue":"7","key":"10.1016\/j.critrevonc.2026.105461_bib24","article-title":"Halicin: A New Approach to Antibacterial Therapy, a Promising Avenue for the Post-Antibiotic Era","volume":"14","author":"El Belghiti","year":"2025","journal-title":"Antibiot. (Basel)"},{"key":"10.1016\/j.critrevonc.2026.105461_bib25","doi-asserted-by":"crossref","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","article-title":"ChEMBL: a large-scale bioactivity database for drug discovery","volume":"40","author":"Gaulton","year":"2012","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"10.1016\/j.critrevonc.2026.105461_bib26","doi-asserted-by":"crossref","first-page":"D1045","DOI":"10.1093\/nar\/gkv1072","article-title":"BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology","volume":"44","author":"Gilson","year":"2016","journal-title":"Nucleic Acids Res."},{"issue":"8036","key":"10.1016\/j.critrevonc.2026.105461_bib27","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1038\/s41586-024-08070-z","article-title":"Machine-guided design of cell-type-targeting cis-regulatory elements","volume":"634","author":"Gosai","year":"2024","journal-title":"Nature"},{"issue":"3","key":"10.1016\/j.critrevonc.2026.105461_bib28","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/ng.3506","article-title":"Integrative approaches for large-scale transcriptome-wide association studies","volume":"48","author":"Gusev","year":"2016","journal-title":"Nat. Genet."},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib29","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1038\/d41573-019-00180-y","article-title":"How many rare diseases are there?","volume":"19","author":"Haendel","year":"2020","journal-title":"Nat. Rev. Drug. Discov."},{"issue":"7829","key":"10.1016\/j.critrevonc.2026.105461_bib30","doi-asserted-by":"crossref","first-page":"E14","DOI":"10.1038\/s41586-020-2766-y","article-title":"Transparency and reproducibility in artificial intelligence","volume":"586","author":"Haibe-Kains","year":"2020","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib31","doi-asserted-by":"crossref","first-page":"6867","DOI":"10.1038\/s41467-024-50903-y","article-title":"De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model","volume":"15","author":"He","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.critrevonc.2026.105461_bib32","doi-asserted-by":"crossref","DOI":"10.2147\/DDDT.S588295","article-title":"Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing","volume":"20","author":"Herdiana","year":"2026","journal-title":"Drug. Des. Devel Ther."},{"key":"10.1016\/j.critrevonc.2026.105461_bib33","series-title":"Proceedings of the 39th International Conference on Machine Learning","first-page":"8867","article-title":"Equivariant Diffusion for Molecule Generation in 3D","author":"Hoogeboom","year":"2022"},{"key":"10.1016\/j.critrevonc.2026.105461_bib34","author":"Huang","year":"2021","journal-title":"Therapeutics data commons Machine learning datasets tasks drug discovery development"},{"issue":"4","key":"10.1016\/j.critrevonc.2026.105461_bib35","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc. Neurol."},{"issue":"10","key":"10.1016\/j.critrevonc.2026.105461_bib36","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s42256-020-00236-4","article-title":"Drug discovery with explainable artificial intelligence","volume":"2","author":"Jim\u00e9nez-Luna","year":"2020","journal-title":"Nat. Mach. Intell."},{"issue":"7873","key":"10.1016\/j.critrevonc.2026.105461_bib37","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"issue":"9","key":"10.1016\/j.critrevonc.2026.105461_bib38","article-title":"Leakage and the reproducibility crisis in machine-learning-based science","volume":"4","author":"Kapoor","year":"2023","journal-title":"Patterns (N. Y)"},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib39","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3390\/applbiosci3010002","article-title":"The Integration of Artificial Intelligence into Clinical Practice","volume":"3","author":"Karalis","year":"2024","journal-title":"Appl. Biosci."},{"issue":"8","key":"10.1016\/j.critrevonc.2026.105461_bib40","doi-asserted-by":"crossref","DOI":"10.1002\/cpz1.217","article-title":"Exploring Chemical Information in PubChem","volume":"1","author":"Kim","year":"2021","journal-title":"Curr. Protoc."},{"issue":"3","key":"10.1016\/j.critrevonc.2026.105461_bib41","doi-asserted-by":"crossref","DOI":"10.5493\/wjem.v14.i3.96042","article-title":"Artificial intelligence as a tool in drug discovery and development","volume":"14","author":"Kokudeva","year":"2024","journal-title":"World J. Exp. Med."},{"key":"10.1016\/j.critrevonc.2026.105461_bib42","article-title":"Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building","volume":"7","author":"Kong","year":"2022","journal-title":"Comput. Human. Behav. Rep."},{"issue":"4","key":"10.1016\/j.critrevonc.2026.105461_bib43","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.240637","article-title":"Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging","volume":"7","author":"Koutsoubis","year":"2025","journal-title":"Radiol. Artif. Intell."},{"issue":"6","key":"10.1016\/j.critrevonc.2026.105461_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.drudis.2024.104009","article-title":"How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons","volume":"29","author":"Kp Jayatunga","year":"2024","journal-title":"Drug. Discov. Today"},{"issue":"23","key":"10.1016\/j.critrevonc.2026.105461_bib45","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.2174\/09298673113209990001","article-title":"Virtual screening strategies in drug discovery: a critical review","volume":"20","author":"Lavecchia","year":"2013","journal-title":"Curr. Med. Chem."},{"issue":"D1","key":"10.1016\/j.critrevonc.2026.105461_bib46","doi-asserted-by":"crossref","first-page":"D1288","DOI":"10.1093\/nar\/gkac813","article-title":"DrugMAP: molecular atlas and pharma-information of all drugs","volume":"51","author":"Li","year":"2023","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib47","doi-asserted-by":"crossref","first-page":"6891","DOI":"10.1038\/s41467-022-34692-w","article-title":"Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor","volume":"13","author":"Li","year":"2022","journal-title":"Nat. Commun."},{"issue":"9","key":"10.1016\/j.critrevonc.2026.105461_bib48","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1021\/acs.jcim.9b00387","article-title":"Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation","volume":"59","author":"Lim","year":"2019","journal-title":"J. Chem. Inf. Model."},{"issue":"12","key":"10.1016\/j.critrevonc.2026.105461_bib49","article-title":"Contrastive learning enhances fairness in pathology artificial intelligence systems","volume":"6","author":"Lin","year":"2025","journal-title":"Cell. Rep. Med."},{"issue":"6637","key":"10.1016\/j.critrevonc.2026.105461_bib50","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib51","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3390\/ai7010026","article-title":"From Algorithm to Medicine: AI in the Discovery and Development of New Drugs","volume":"7","author":"Lopes","year":"2026","journal-title":"AI"},{"issue":"8","key":"10.1016\/j.critrevonc.2026.105461_bib52","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1038\/s41592-019-0494-8","article-title":"scGen predicts single-cell perturbation responses","volume":"16","author":"Lotfollahi","year":"2019","journal-title":"Nat. Methods"},{"issue":"6","key":"10.1016\/j.critrevonc.2026.105461_bib53","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac409","article-title":"BioGPT: generative pre-trained transformer for biomedical text generation and mining","volume":"23","author":"Luo","year":"2022","journal-title":"Brief. Bioinform"},{"issue":"4","key":"10.1016\/j.critrevonc.2026.105461_bib54","article-title":"The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century","volume":"11","author":"Maleki Varnosfaderani","year":"2024","journal-title":"Bioeng. (Basel)"},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib55","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1038\/nbt1186","article-title":"Statistical practice in high-throughput screening data analysis","volume":"24","author":"Malo","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.critrevonc.2026.105461_bib56","doi-asserted-by":"crossref","DOI":"10.3389\/fragi.2023.1057204","article-title":"Towards AI-driven longevity research: An overview","volume":"4","author":"Marino","year":"2023","journal-title":"Front. Aging"},{"issue":"9","key":"10.1016\/j.critrevonc.2026.105461_bib57","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1007\/s40265-021-01516-y","article-title":"Evinacumab: First Approval","volume":"81","author":"Markham","year":"2021","journal-title":"Drugs"},{"key":"10.1016\/j.critrevonc.2026.105461_bib58","series-title":"How Is AI Used In Healthcare - 5 Powerful Real-World Examples That Show The Latest Advances","author":"Marr","year":"2018"},{"key":"10.1016\/j.critrevonc.2026.105461_bib59","doi-asserted-by":"crossref","first-page":"80","DOI":"10.3389\/fenvs.2015.00080","article-title":"DeepTox: toxicity prediction using deep learning","volume":"3","author":"Mayr","year":"2016","journal-title":"Front. Environ. Sci."},{"key":"10.1016\/j.critrevonc.2026.105461_bib60","first-page":"03864","volume":"2103","author":"Maziarz","year":"2021","journal-title":"Learn. extend Mol. scaffolds Struct. motifs. arXiv Prepr. arXiv"},{"issue":"D1","key":"10.1016\/j.critrevonc.2026.105461_bib61","doi-asserted-by":"crossref","first-page":"D930","DOI":"10.1093\/nar\/gky1075","article-title":"ChEMBL: towards direct deposition of bioassay data","volume":"47","author":"Mendez","year":"2019","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib62","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.hfc.2021.11.004","article-title":"Using Artificial Intelligence to Better Predict and Develop Biomarkers","volume":"18","author":"Michelhaugh","year":"2022","journal-title":"Heart Fail. Clin."},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib63","doi-asserted-by":"crossref","DOI":"10.1038\/srep26094","article-title":"Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records","volume":"6","author":"Miotto","year":"2016","journal-title":"Sci. Rep."},{"issue":"5","key":"10.1016\/j.critrevonc.2026.105461_bib64","doi-asserted-by":"crossref","DOI":"10.3390\/ijms24054274","article-title":"Artificial Intelligence-Based Computational Screening and Functional Assays Identify Candidate Small Molecule Antagonists of PTPmu-Dependent Adhesion","volume":"24","author":"Molyneaux","year":"2023","journal-title":"Int. J. Mol. Sci."},{"issue":"7673","key":"10.1016\/j.critrevonc.2026.105461_bib65","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1038\/549445a","article-title":"The drug-maker's guide to the galaxy","volume":"549","author":"Mullard","year":"2017","journal-title":"Nature"},{"issue":"9","key":"10.1016\/j.critrevonc.2026.105461_bib66","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.3390\/ph18091271","article-title":"Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes","volume":"18","author":"Niazi","year":"2025","journal-title":"Pharmaceuticals"},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib67","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s40364-025-00758-2","article-title":"Integrating artificial intelligence in drug discovery and early drug development: a transformative approach","volume":"13","author":"Ocana","year":"2025","journal-title":"Biomark. Res."},{"issue":"13","key":"10.1016\/j.critrevonc.2026.105461_bib68","doi-asserted-by":"crossref","DOI":"10.1002\/cbic.202200776","article-title":"Structure-Based Drug Discovery with Deep Learning","volume":"24","author":"\u00d6z\u00e7elik","year":"2023","journal-title":"Chembiochem"},{"issue":"7","key":"10.1016\/j.critrevonc.2026.105461_bib69","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1038\/s12276-025-01487-0","article-title":"Perturbomics: CRISPR\u2013Cas screening-based functional genomics approach for drug target discovery","volume":"57","author":"Park","year":"2025","journal-title":"Exp. & Mol. Med."},{"key":"10.1016\/j.critrevonc.2026.105461_bib70","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2020.565644","article-title":"Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models","volume":"11","author":"Polykovskiy","year":"2020","journal-title":"Front. Pharmacol."},{"issue":"7","key":"10.1016\/j.critrevonc.2026.105461_bib71","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aap7885","article-title":"Deep reinforcement learning for de novo drug design","volume":"4","author":"Popova","year":"2018","journal-title":"Sci. Adv."},{"issue":"9","key":"10.1016\/j.critrevonc.2026.105461_bib72","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1038\/nrd3439-c1","article-title":"Believe it or not: how much can we rely on published data on potential drug targets?","volume":"10","author":"Prinz","year":"2011","journal-title":"Nat. Rev. Drug. Discov."},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib73","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/nrd.2018.168","article-title":"Drug repurposing: progress, challenges and recommendations","volume":"18","author":"Pushpakom","year":"2019","journal-title":"Nat. Rev. Drug. Discov."},{"issue":"8","key":"10.1016\/j.critrevonc.2026.105461_bib74","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1021\/acs.jcim.7b00146","article-title":"Is Multitask Deep Learning Practical for Pharma?","volume":"57","author":"Ramsundar","year":"2017","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib75","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/s41587-024-02143-0","article-title":"A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models","volume":"43","author":"Ren","year":"2025","journal-title":"Nat. Biotechnol."},{"issue":"10223","key":"10.1016\/j.critrevonc.2026.105461_bib76","doi-asserted-by":"crossref","first-page":"e30","DOI":"10.1016\/S0140-6736(20)30304-4","article-title":"Baricitinib as potential treatment for 2019-nCoV acute respiratory disease","volume":"395","author":"Richardson","year":"2020","journal-title":"Lancet"},{"issue":"4","key":"10.1016\/j.critrevonc.2026.105461_bib77","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.tips.2021.01.003","article-title":"Drug Repurposing for Rare Diseases","volume":"42","author":"Roessler","year":"2021","journal-title":"Trends Pharmacol. Sci."},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib78","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.therap.2020.02.007","article-title":"Drug repositioning for rare diseases: Knowledge-based success stories","volume":"75","author":"Scherman","year":"2020","journal-title":"Therapie"},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib79","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/nrd.2017.232","article-title":"Automating drug discovery","volume":"17","author":"Schneider","year":"2018","journal-title":"Nat Rev Drug Discov"},{"issue":"5","key":"10.1016\/j.critrevonc.2026.105461_bib80","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/s41573-019-0050-3","article-title":"Rethinking drug design in the artificial intelligence era","volume":"19","author":"Schneider","year":"2020","journal-title":"Nat. Rev. Drug. Discov."},{"key":"10.1016\/j.critrevonc.2026.105461_bib81","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/978-3-030-16391-4_11","article-title":"Artificial Intelligence and Personalized Medicine","volume":"178","author":"Schork","year":"2019","journal-title":"Cancer Treat. Res."},{"issue":"10","key":"10.1016\/j.critrevonc.2026.105461_bib82","doi-asserted-by":"crossref","DOI":"10.3390\/pharmaceutics16101328","article-title":"Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine","volume":"16","author":"Serrano","year":"2024","journal-title":"Pharmaceutics"},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib83","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1186\/s12967-019-1864-9","article-title":"Are innovation and new technologies in precision medicine paving a new era in patients centric care?","volume":"17","author":"Seyhan","year":"2019","journal-title":"J. Transl. Med."},{"key":"10.1016\/j.critrevonc.2026.105461_bib84","series-title":"The New Era of Precision Medicine","first-page":"83","article-title":"Chapter 4 - Precision medicine: success stories and challenges from science to implementation","author":"Seyhan","year":"2024"},{"key":"10.1016\/j.critrevonc.2026.105461_bib85","unstructured":"Shanehsazzadeh, A., Bachas, S., Kasun, G., Sutton, J.M., Steiger, A.K., Shuai, R., Kohnert, C., Morehead, A., Brown, A., Chung, C., Luton, B.K., Diaz, N., McPartlon, M., Knight, B., Radach, M., Bateman, K., Spencer, D.A., Cejovic, J., Kopec-Belliveau, G., Haile, R., Yassine, E., McCloskey, C., Natividad, M., Chapman, D., Stojanovic, L., Rakocevic, G., Hannum, G., Yapici, E., Moran, K., Caguiat, R., Abdulhaqq, S., Guo, Z., Klug, L.R., Gander, M., Meier, J., 2023. Unlocking <em>de novo<\/em> antibody design with generative artificial intelligence. bioRxiv, 2023.2001.2008.523187."},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib86","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s13321-024-00829-w","article-title":"Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery","volume":"16","author":"Shen","year":"2024","journal-title":"J. Chemin.-."},{"issue":"4","key":"10.1016\/j.critrevonc.2026.105461_bib87","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.3897\/pharmacia.70.e114935","article-title":"Lead drug discovery from imidazolinone derivatives with Aurora kinase inhibitors","volume":"70","author":"Sivakumar","year":"2023","journal-title":"PHARMACIA"},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib88","doi-asserted-by":"crossref","first-page":"138","DOI":"10.14218\/ERHM.2023.00048","article-title":"Applications of Artificial Intelligence in Medicine","volume":"9","author":"Srivastava","year":"2024","journal-title":"Explor. Res. Hypothesis Med."},{"key":"10.1016\/j.critrevonc.2026.105461_bib89","doi-asserted-by":"crossref","unstructured":"Stark, H., Faltings, F., Choi, M., Xie, Y., Hur, E., O\u2019Donnell, T., Bushuiev, A., U\u00e7ar, T., Passaro, S., Mao, W., Reveiz, M., Bushuiev, R., Pluskal, T., Sivic, J., Kreis, K., Vahdat, A., Ray, S., Goldstein, J.T., Savinov, A., Hambalek, J.A., Gupta, A., Taquiri-Diaz, D.A., Zhang, Y., Hatstat, A.K., Arada, A., Kim, N.H., Tackie-Yarboi, E., Boselli, D., Schnaider, L., Liu, C.C., Li, G.-W., Hnisz, D., Sabatini, D.M., DeGrado, W.F., Wohlwend, J., Corso, G., Barzilay, R., Jaakkola, T., 2025. BoltzGen: Toward Universal Binder Design. bioRxiv, 2025.2011.2020.689494.","DOI":"10.1101\/2025.11.20.689494"},{"issue":"8","key":"10.1016\/j.critrevonc.2026.105461_bib90","doi-asserted-by":"crossref","DOI":"10.15252\/emmm.202012697","article-title":"Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients","volume":"12","author":"Stebbing","year":"2020","journal-title":"EMBO Mol. Med."},{"issue":"4","key":"10.1016\/j.critrevonc.2026.105461_bib91","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","article-title":"A Deep Learning Approach to Antibiotic Discovery","volume":"180","author":"Stokes","year":"2020","journal-title":"Cell"},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib92","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1038\/s41746-025-01886-7","article-title":"A scoping review of artificial intelligence applications in clinical trial risk assessment","volume":"8","author":"Teodoro","year":"2025","journal-title":"NPJ Digit. Med."},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib93","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s00280-020-04184-z","article-title":"Population pharmacokinetics of ATR inhibitor berzosertib in phase I studies for different cancer types","volume":"87","author":"Terranova","year":"2021","journal-title":"Cancer Chemother. Pharmacol."},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib94","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"issue":"3","key":"10.1016\/j.critrevonc.2026.105461_bib95","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.cell.2017.06.010","article-title":"Defining a Cancer Dependency Map","volume":"170","author":"Tsherniak","year":"2017","journal-title":"Cell"},{"issue":"3","key":"10.1016\/j.critrevonc.2026.105461_bib96","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1109\/TCBB.2018.2841396","article-title":"Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images","volume":"16","author":"Urban","year":"2019","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform"},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib97","article-title":"Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery","volume":"14","author":"Visan","year":"2024","journal-title":"Life (Basel)"},{"key":"10.1016\/j.critrevonc.2026.105461_bib98","author":"Wallach","year":"2015","journal-title":"At. A Deep. Convolutional Neural Netw. Bioactivity Predict. Struct. -Based Drug. Discov."},{"issue":"7976","key":"10.1016\/j.critrevonc.2026.105461_bib99","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1038\/s41586-023-06415-8","article-title":"De novo design of protein structure and function with RFdiffusion","volume":"620","author":"Watson","year":"2023","journal-title":"Nature"},{"issue":"D1","key":"10.1016\/j.critrevonc.2026.105461_bib100","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: a major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"10.1016\/j.critrevonc.2026.105461_bib101","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: a benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci."},{"issue":"W1","key":"10.1016\/j.critrevonc.2026.105461_bib102","doi-asserted-by":"crossref","first-page":"W5","DOI":"10.1093\/nar\/gkab255","article-title":"ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties","volume":"49","author":"Xiong","year":"2021","journal-title":"Nucleic Acids Res."},{"issue":"8","key":"10.1016\/j.critrevonc.2026.105461_bib103","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.1038\/s41591-025-03743-2","article-title":"A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial","volume":"31","author":"Xu","year":"2025","journal-title":"Nat. Med."},{"issue":"8","key":"10.1016\/j.critrevonc.2026.105461_bib104","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.1038\/s41591-025-03743-2","article-title":"A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial","volume":"31","author":"Xu","year":"2025","journal-title":"Nat. Med."},{"issue":"1","key":"10.1016\/j.critrevonc.2026.105461_bib105","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1080\/14686996.2017.1401424","article-title":"ChemTS: an efficient python library for de novo molecular generation","volume":"18","author":"Yang","year":"2017","journal-title":"Sci. Technol. Adv. Mater."},{"issue":"4","key":"10.1016\/j.critrevonc.2026.105461_bib106","doi-asserted-by":"crossref","DOI":"10.2196\/25759","article-title":"Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review","volume":"23","author":"Yin","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"10.1016\/j.critrevonc.2026.105461_bib107","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.compbiolchem.2019.03.016","article-title":"Predicting drug-target interaction network using deep learning model","volume":"80","author":"You","year":"2019","journal-title":"Comput. Biol. Chem."},{"key":"10.1016\/j.critrevonc.2026.105461_bib108","article-title":"The Application of AI in Drug Discovery and Early Development: Impact and Challenges","author":"Zemnou","year":"2026","journal-title":"Prepr Prepr"},{"issue":"7978","key":"10.1016\/j.critrevonc.2026.105461_bib109","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1038\/s41586-023-06127-z","article-title":"Algorithm for optimized mRNA design improves stability and immunogenicity","volume":"621","author":"Zhang","year":"2023","journal-title":"Nature"},{"issue":"9","key":"10.1016\/j.critrevonc.2026.105461_bib110","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","article-title":"Deep learning enables rapid identification of potent DDR1 kinase inhibitors","volume":"37","author":"Zhavoronkov","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.critrevonc.2026.105461_bib111","doi-asserted-by":"crossref","DOI":"10.3389\/fbinf.2026.1755843","article-title":"Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications","volume":"6","author":"Zhou","year":"2026","journal-title":"Front. Bioinform"},{"issue":"11","key":"10.1016\/j.critrevonc.2026.105461_bib112","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpha.2024.101160","article-title":"AI comes to the Nobel Prize and drug discovery","volume":"14","author":"Zhou","year":"2024","journal-title":"J. Pharm. Anal."},{"issue":"8","key":"10.1016\/j.critrevonc.2026.105461_bib113","doi-asserted-by":"crossref","first-page":"2490","DOI":"10.1038\/s41591-025-03832-2","article-title":"AI-enabled drug discovery reaches clinical milestone","volume":"31","author":"Zitnik","year":"2025","journal-title":"Nat. Med."}],"container-title":["Critical Reviews in Oncology\/Hematology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1040842826003483?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1040842826003483?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T05:47:20Z","timestamp":1793944040000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1040842826003483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":113,"alternative-id":["S1040842826003483"],"URL":"https:\/\/doi.org\/10.1016\/j.critrevonc.2026.105461","relation":{},"ISSN":["1040-8428"],"issn-type":[{"value":"1040-8428","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence in drug discovery and development: current landscape, challenges, and future perspectives","name":"articletitle","label":"Article Title"},{"value":"Critical Reviews in Oncology\/Hematology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.critrevonc.2026.105461","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"105461"}}