{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T17:07:05Z","timestamp":1774285625374,"version":"3.50.1"},"reference-count":142,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-024-00192-7","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T17:40:10Z","timestamp":1731606010000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Beyond boundaries: exploring the transformative power of AI in pharmaceuticals"],"prefix":"10.1007","volume":"4","author":[{"given":"Gurparsad Singh","family":"Suri","sequence":"first","affiliation":[]},{"given":"Gurleen","family":"Kaur","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5113-3167","authenticated-orcid":false,"given":"Dheeraj","family":"Shinde","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"192_CR1","first-page":"2323","volume":"150","author":"S Ralston","year":"2017","unstructured":"Ralston S. Pre-development attrition of pharmaceuticals: how to identify the bad actors early. Toxicol Sci. 2017;150:2323.","journal-title":"Toxicol Sci"},{"issue":"3","key":"192_CR2","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1080\/17460441.2020.1691995","volume":"15","author":"S Lobo","year":"2020","unstructured":"Lobo S. Is there enough focus on lipophilicity in drug discovery? Expert Opin Drug Discov. 2020;15(3):261\u20133.","journal-title":"Expert Opin Drug Discov"},{"issue":"2","key":"192_CR3","doi-asserted-by":"publisher","first-page":"165","DOI":"10.2174\/138920006775541552","volume":"7","author":"S Singh","year":"2006","unstructured":"Singh S. Preclinical pharmacokinetics: an approach towards safer and efficacious drugs. Curr Drug Metab. 2006;7(2):165\u201382.","journal-title":"Curr Drug Metab"},{"issue":"1","key":"192_CR4","doi-asserted-by":"publisher","first-page":"18911","DOI":"10.1038\/s41598-019-54849-w","volume":"9","author":"AD Hingorani","year":"2019","unstructured":"Hingorani AD, Kuan V, Finan C, Kruger FA, Gaulton A, Chopade S, et al. Improving the odds of drug development success through human genomics: modelling study. Sci Rep. 2019;9(1):18911.","journal-title":"Sci Rep"},{"issue":"9","key":"192_CR5","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.tips.2023.06.010","volume":"44","author":"FW Pun","year":"2023","unstructured":"Pun FW, Ozerov IV, Zhavoronkov A. AI-powered therapeutic target discovery. Trends Pharmacol Sci. 2023;44(9):561\u201372.","journal-title":"Trends Pharmacol Sci"},{"issue":"8","key":"192_CR6","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.tips.2019.05.005","volume":"40","author":"S Harrer","year":"2019","unstructured":"Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40(8):577\u201391.","journal-title":"Trends Pharmacol Sci"},{"key":"192_CR7","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1200\/CCI.19.00079","volume":"4","author":"JT Beck","year":"2020","unstructured":"Beck JT, Rammage M, Jackson GP, Preininger AM, Dankwa-Mullan I, Roebuck MC, et al. Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center. JCO Clin Cancer Inform. 2020;4:50\u20139.","journal-title":"JCO Clin Cancer Inform"},{"issue":"7805","key":"192_CR8","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1038\/s41586-020-2117-z","volume":"580","author":"C Gorgulla","year":"2020","unstructured":"Gorgulla C, Boeszoermenyi A, Wang ZF, Fischer PD, Coote PW, Padmanabha Das KM, et al. An open-source drug discovery platform enables ultra-large virtual screens. Nature. 2020;580(7805):663\u20138.","journal-title":"Nature"},{"key":"192_CR9","unstructured":"Athos Therapeutics. Drug Pipeline. https:\/\/athostx.com\/pipeline\/. Accessed 17 Aug 2024."},{"key":"192_CR10","unstructured":"BenevolentAI. Ulcerative colitis (BEN-8744). https:\/\/www.benevolent.com\/pipeline\/ulcerative-colitis\/. Accessed 17 Aug 2024"},{"key":"192_CR11","unstructured":"Compugen. Pipeline. https:\/\/cgen.com\/pipeline\/. Accessed 17 Aug 2024"},{"key":"192_CR12","unstructured":"HiFiBiO Therapeutics Inc. Pipeline. https:\/\/hifibio.com\/pipeline\/pipeline-overview\/. Accessed 17 Aug 2024"},{"key":"192_CR13","unstructured":"HotSpot Therapeutics Inc. Pipeline of Novel Targets. https:\/\/www.hotspotthera.com\/pipeline\/. Accessed 17 Aug 2024"},{"key":"192_CR14","unstructured":"Insilico Medicine. New Milestone in AI Drug Discovery: First Generative AI Drug Begins Phase II Trials with Patients. https:\/\/insilico.com\/blog\/first_phase2. Accessed 17 Aug 2024"},{"key":"192_CR15","unstructured":"Andrew Leber RHNTJ and JBRNIBV. Safety and Tolerability of NIM-1324, an Oral, Once-daily LANCL2 Agonist, in a Randomized, Double-Blind, Placebo-Controlled Phase 1 Study in Normal Healthy Volunteers. https:\/\/acrabstracts.org\/abstract\/safety-and-tolerability-of-nim-1324-an-oral-once-daily-lancl2-agonist-in-a-randomized-double-blind-placebo-controlled-phase-1-study-in-normal-healthy-volunteers\/. Accessed 17 Aug 2024"},{"key":"192_CR16","unstructured":"Recursion Inc. Our Leading AI-Driven Drug Discovery Pipeline. Available from: https:\/\/www.recursion.com\/pipeline. Accessed 17 Aug 2024"},{"key":"192_CR17","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.csbj.2016.04.004","volume":"14","author":"T Katsila","year":"2016","unstructured":"Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT. Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J. 2016;14:177\u201384.","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"192_CR18","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41392-022-00994-0","volume":"7","author":"Y You","year":"2022","unstructured":"You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther. 2022;7(1):156.","journal-title":"Signal Transduct Target Ther"},{"key":"192_CR19","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2022.914017","volume":"14","author":"FW Pun","year":"2022","unstructured":"Pun FW, Liu BHM, Long X, Leung HW, Leung GHD, Mewborne QT, et al. Identification of therapeutic targets for amyotrophic lateral sclerosis using PandaOmics\u2014an AI-enabled biological target discovery platform. Front Aging Neurosci. 2022;14: 914017.","journal-title":"Front Aging Neurosci"},{"issue":"6","key":"192_CR20","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1039\/D2SC05709C","volume":"14","author":"F Ren","year":"2023","unstructured":"Ren F, Ding X, Zheng M, Korzinkin M, Cai X, Zhu W, et al. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor. Chem Sci. 2023;14(6):1443\u201352.","journal-title":"Chem Sci"},{"issue":"6","key":"192_CR21","doi-asserted-by":"publisher","first-page":"bbac409","DOI":"10.1093\/bib\/bbac409","volume":"23","author":"R Luo","year":"2022","unstructured":"Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform. 2022;23(6):bbac409.","journal-title":"Brief Bioinform"},{"key":"192_CR22","unstructured":"INSILICO MEDICINE. Insilico Medicine brings AI-powered \u201cChatPandaGPT\u201d to its target discovery platform; 2023. https:\/\/www.eurekalert.org\/news-releases\/982543."},{"issue":"7873","key":"192_CR23","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583\u20139.","journal-title":"Nature"},{"key":"192_CR24","unstructured":"Bharath Ramsundar. Molecular Machine Learning with DeepChem. [Stanford]: STANFORD UNIVERSITY; 2018. https:\/\/www.proquest.com\/openview\/9c0e06a343233b48d962991d19873ed8\/1?pq-origsite=gscholar&cbl=18750&diss=y. Accessed 20 Dec 2023."},{"issue":"8","key":"192_CR25","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","volume":"33","author":"B Alipanahi","year":"2015","unstructured":"Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015;33(8):831\u20138.","journal-title":"Nat Biotechnol"},{"issue":"2","key":"192_CR26","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1021\/acs.jctc.8b01010","volume":"15","author":"X Ding","year":"2019","unstructured":"Ding X, Vilseck JZ, Brooks CL. Fast solver for large scale multistate bennett acceptance ratio equations. J Chem Theory Comput. 2019;15(2):799\u2013802.","journal-title":"J Chem Theory Comput"},{"issue":"9","key":"192_CR27","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1039\/C9SC03414E","volume":"11","author":"AS Rifaioglu","year":"2020","unstructured":"Rifaioglu AS, Nalbat E, Atalay V, Martin MJ, Cetin-Atalay R, Do\u011fan T. DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations. Chem Sci. 2020;11(9):2531\u201357.","journal-title":"Chem Sci"},{"issue":"6","key":"192_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007129","volume":"15","author":"I Lee","year":"2019","unstructured":"Lee I, Keum J, Nam H. DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput Biol. 2019;15(6): e1007129.","journal-title":"PLoS Comput Biol"},{"issue":"22\u201323","key":"192_CR29","doi-asserted-by":"publisher","first-page":"5545","DOI":"10.1093\/bioinformatics\/btaa1005","volume":"36","author":"K Huang","year":"2021","unstructured":"Huang K, Fu T, Glass LM, Zitnik M, Xiao C, Sun J. DeepPurpose: a deep learning library for drug-target interaction prediction. Bioinformatics. 2021;36(22\u201323):5545\u20137.","journal-title":"Bioinformatics"},{"issue":"1","key":"192_CR30","doi-asserted-by":"publisher","first-page":"3128","DOI":"10.1038\/s41598-021-82612-7","volume":"11","author":"YC Tang","year":"2021","unstructured":"Tang YC, Gottlieb A. Explainable drug sensitivity prediction through cancer pathway enrichment. Sci Rep. 2021;11(1):3128.","journal-title":"Sci Rep"},{"issue":"2","key":"192_CR31","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R G\u00f3mez-Bombarelli","year":"2018","unstructured":"G\u00f3mez-Bombarelli R, Wei JN, Duvenaud D, Hern\u00e1ndez-Lobato JM, S\u00e1nchez-Lengeling B, Sheberla D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4(2):268\u201376.","journal-title":"ACS Cent Sci"},{"issue":"7800","key":"192_CR32","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1038\/s41586-020-2027-0","volume":"579","author":"RM Stein","year":"2020","unstructured":"Stein RM, Kang HJ, McCorvy JD, Glatfelter GC, Jones AJ, Che T, et al. Virtual discovery of melatonin receptor ligands to modulate circadian rhythms. Nature. 2020;579(7800):609\u201314.","journal-title":"Nature"},{"issue":"7743","key":"192_CR33","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41586-019-0917-9","volume":"566","author":"J Lyu","year":"2019","unstructured":"Lyu J, Wang S, Balius TE, Singh I, Levit A, Moroz YS, et al. Ultra-large library docking for discovering new chemotypes. Nature. 2019;566(7743):224\u20139.","journal-title":"Nature"},{"issue":"11","key":"192_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2020.101681","volume":"23","author":"OO Grygorenko","year":"2020","unstructured":"Grygorenko OO, Radchenko DS, Dziuba I, Chuprina A, Gubina KE, Moroz YS. Generating multibillion chemical space of readily accessible screening compounds. iScience. 2020;23(11): 101681.","journal-title":"iScience"},{"issue":"1","key":"192_CR35","doi-asserted-by":"publisher","first-page":"5736","DOI":"10.1038\/s41467-023-41512-2","volume":"14","author":"G Turon","year":"2023","unstructured":"Turon G, Hlozek J, Woodland JG, Kumar A, Chibale K, Duran-Frigola M. First fully-automated AI\/ML virtual screening cascade implemented at a drug discovery centre in Africa. Nat Commun. 2023;14(1):5736.","journal-title":"Nat Commun"},{"issue":"8","key":"192_CR36","doi-asserted-by":"publisher","first-page":"3835","DOI":"10.1021\/acs.jcim.1c00653","volume":"61","author":"G Amendola","year":"2021","unstructured":"Amendola G, Cosconati S. PyRMD: a new fully automated AI-powered ligand-based virtual screening tool. J Chem Inf Model. 2021;61(8):3835\u201345.","journal-title":"J Chem Inf Model"},{"issue":"1","key":"192_CR37","doi-asserted-by":"publisher","first-page":"4536","DOI":"10.1038\/s41467-024-48837-6","volume":"15","author":"P Bryant","year":"2024","unstructured":"Bryant P, Kelkar A, Guljas A, Clementi C, No\u00e9 F. Structure prediction of protein-ligand complexes from sequence information with Umol. Nat Commun. 2024;15(1):4536.","journal-title":"Nat Commun"},{"key":"192_CR38","unstructured":"Weller JA, Rohs R. DrugHIVE: Target-specific spatial drug design and optimization with a hierarchical generative model. bioRxiv; 2024. http:\/\/biorxiv.org\/content\/early\/2024\/03\/17\/2023.12.22.573155.abstract"},{"key":"192_CR39","unstructured":"Pei Q, Gao K, Wu L, Zhu J, Xia Y, Xie S, et al. FABind: fast and accurate protein-ligand binding. arXiv e-prints; 2023. arXiv:2310.06763."},{"key":"192_CR40","unstructured":"Ziv Y, Marsden B, Deane CM. MolSnapper: conditioning diffusion for structure based drug design. bioRxiv; 2024. http:\/\/biorxiv.org\/content\/early\/2024\/03\/30\/2024.03.28.586278.abstract"},{"issue":"1","key":"192_CR41","doi-asserted-by":"publisher","first-page":"2657","DOI":"10.1038\/s41467-024-46569-1","volume":"15","author":"L Huang","year":"2024","unstructured":"Huang L, Xu T, Yu Y, Zhao P, Chen X, Han J, et al. A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets. Nat Commun. 2024;15(1):2657.","journal-title":"Nat Commun"},{"key":"192_CR42","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.202400238","author":"V Tran-Nguyen","year":"2024","unstructured":"Tran-Nguyen V, Camproux A, Taboureau O. ClassyPose: a machine-learning classification model for ligand pose selection applied to virtual screening in drug discovery. Adv Intell Syst. 2024. https:\/\/doi.org\/10.1002\/aisy.202400238.","journal-title":"Adv Intell Syst"},{"issue":"1","key":"192_CR43","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1038\/s41467-024-45461-2","volume":"15","author":"W Lu","year":"2024","unstructured":"Lu W, Zhang J, Huang W, Zhang Z, Jia X, Wang Z, et al. DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model. Nat Commun. 2024;15(1):1071.","journal-title":"Nat Commun"},{"issue":"2","key":"192_CR44","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1039\/D3RA08147H","volume":"14","author":"T Voitsitskyi","year":"2024","unstructured":"Voitsitskyi T, Bdzhola V, Stratiichuk R, Koleiev I, Ostrovsky Z, Vozniak V, et al. Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets. RSC Adv. 2024;14(2):1341\u201353.","journal-title":"RSC Adv"},{"key":"192_CR45","unstructured":"Peng X, Luo S, Guan J, Xie Q, Peng J, Ma J. Pocket2Mol: efficient molecular sampling based on 3D protein pockets. arXiv e-prints; 2022. arXiv:2205.07249."},{"key":"192_CR46","unstructured":"Corso G, St\u00e4rk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv e-prints; 2022. arXiv:2210.01776."},{"issue":"9","key":"192_CR47","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1038\/s43588-023-00511-5","volume":"3","author":"X Zhang","year":"2023","unstructured":"Zhang X, Zhang O, Shen C, Qu W, Chen S, Cao H, et al. Efficient and accurate large library ligand docking with KarmaDock. Nat Comput Sci. 2023;3(9):789\u2013804.","journal-title":"Nat Comput Sci"},{"issue":"32","key":"192_CR48","doi-asserted-by":"publisher","first-page":"29143","DOI":"10.1021\/acsomega.3c02249","volume":"8","author":"DP McDougal","year":"2023","unstructured":"McDougal DP, Rajapaksha H, Pederick JL, Bruning JB. warpDOCK: large-scale virtual drug discovery using cloud infrastructure. ACS Omega. 2023;8(32):29143\u20139.","journal-title":"ACS Omega"},{"key":"192_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107283","volume":"164","author":"Y Shi","year":"2023","unstructured":"Shi Y, Zhang X, Yang Y, Cai T, Peng C, Wu L, et al. D3CARP: a comprehensive platform with multiple-conformation based docking, ligand similarity search and deep learning approaches for target prediction and virtual screening. Comput Biol Med. 2023;164: 107283.","journal-title":"Comput Biol Med"},{"issue":"13","key":"192_CR50","doi-asserted-by":"publisher","first-page":"3661","DOI":"10.1039\/D1SC06946B","volume":"13","author":"S Moon","year":"2022","unstructured":"Moon S, Zhung W, Yang S, Lim J, Kim WY. PIGNet: a physics-informed deep learning model toward generalized drug\u2013target interaction predictions. Chem Sci. 2022;13(13):3661\u201373.","journal-title":"Chem Sci"},{"key":"192_CR51","unstructured":"St\u00e4rk H, Ganea OE, Pattanaik L, Barzilay R, Jaakkola T. EquiBind: geometric deep learning for drug binding structure prediction. arXiv e-prints; 2022. arXiv:2202.05146."},{"key":"192_CR52","doi-asserted-by":"crossref","unstructured":"Boitreaud J, Oliver C, Mallet V, Waldisp\u00fchl J. OptiMol\u202f: optimization of binding affinities in chemical space for drug discovery. bioRxiv; 2020. http:\/\/biorxiv.org\/content\/early\/2020\/06\/16\/2020.05.23.112201.abstract","DOI":"10.1101\/2020.05.23.112201"},{"issue":"18","key":"192_CR53","doi-asserted-by":"publisher","first-page":"4300","DOI":"10.1021\/acs.jcim.2c00695","volume":"62","author":"AV Fassio","year":"2022","unstructured":"Fassio AV, Shub L, Ponzoni L, McKinley J, O\u2019Meara MJ, Ferreira RS, et al. Prioritizing virtual screening with interpretable interaction fingerprints. J Chem Inf Model. 2022;62(18):4300\u201318.","journal-title":"J Chem Inf Model"},{"issue":"6","key":"192_CR54","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1021\/acscentsci.0c00229","volume":"6","author":"F Gentile","year":"2020","unstructured":"Gentile F, Agrawal V, Hsing M, Ton AT, Ban F, Norinder U, et al. Deep docking: a deep learning platform for augmentation of structure based drug discovery. ACS Cent Sci. 2020;6(6):939\u201349.","journal-title":"ACS Cent Sci"},{"issue":"3","key":"192_CR55","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1038\/s41596-021-00659-2","volume":"17","author":"F Gentile","year":"2022","unstructured":"Gentile F, Yaacoub JC, Gleave J, Fernandez M, Ton AT, Ban F, et al. Artificial intelligence\u2013enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc. 2022;17(3):672\u201397.","journal-title":"Nat Protoc"},{"key":"192_CR56","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.4155\/fmc-2023-0152","volume":"15","author":"JT Moreira-Filho","year":"2023","unstructured":"Moreira-Filho JT, Neves BJ, Cajas RA, de Moraes J, Andrade CH. Artificial intelligence-guided approach for efficient virtual screening of hits against Schistosoma mansoni. Future Med Chem. 2023;15:2033\u201350.","journal-title":"Future Med Chem"},{"issue":"8","key":"192_CR57","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1038\/nrd2030","volume":"5","author":"HA Ghofrani","year":"2006","unstructured":"Ghofrani HA, Osterloh IH, Grimminger F. Sildenafil: from angina to erectile dysfunction to pulmonary hypertension and beyond. Nat Rev Drug Discov. 2006;5(8):689\u2013702.","journal-title":"Nat Rev Drug Discov"},{"issue":"1","key":"192_CR58","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1186\/s13321-019-0394-z","volume":"11","author":"F Wang","year":"2019","unstructured":"Wang F, Wu FX, Li CZ, Jia CY, Su SW, Hao GF, et al. ACID: a free tool for drug repurposing using consensus inverse docking strategy. J Cheminform. 2019;11(1):73.","journal-title":"J Cheminform"},{"issue":"2","key":"192_CR59","doi-asserted-by":"publisher","first-page":"59","DOI":"10.12793\/tcp.2019.27.2.59","volume":"27","author":"K Park","year":"2019","unstructured":"Park K. A review of computational drug repurposing. Transl Clin Pharmacol. 2019;27(2):59.","journal-title":"Transl Clin Pharmacol"},{"issue":"3","key":"192_CR60","doi-asserted-by":"publisher","first-page":"264","DOI":"10.2174\/1570180819666220901170016","volume":"20","author":"KK Kaushik","year":"2023","unstructured":"Kaushik KK, Mazumder R, Debnath A, Patel M. A brief study on drug repurposing: new way of boosting drug discovery. Lett Drug Des Discov. 2023;20(3):264\u201378.","journal-title":"Lett Drug Des Discov"},{"issue":"10","key":"192_CR61","doi-asserted-by":"publisher","first-page":"875","DOI":"10.3390\/toxics11100875","volume":"11","author":"M Rao","year":"2023","unstructured":"Rao M, McDuffie E, Sachs C. Artificial intelligence\/machine learning-driven small molecule repurposing via off-target prediction and transcriptomics. Toxics. 2023;11(10):875.","journal-title":"Toxics"},{"issue":"1","key":"192_CR62","doi-asserted-by":"publisher","first-page":"19358","DOI":"10.1038\/s41598-023-46648-1","volume":"13","author":"M Tanabe","year":"2023","unstructured":"Tanabe M, Sakate R, Nakabayashi J, Tsumura K, Ohira S, Iwato K, et al. A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases. Sci Rep. 2023;13(1):19358.","journal-title":"Sci Rep"},{"issue":"9","key":"192_CR63","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1016\/j.tips.2019.07.005","volume":"40","author":"AO Basile","year":"2019","unstructured":"Basile AO, Yahi A, Tatonetti NP. Artificial intelligence for drug toxicity and safety. Trends Pharmacol Sci. 2019;40(9):624\u201335.","journal-title":"Trends Pharmacol Sci"},{"issue":"4","key":"192_CR64","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1038\/nrd4539","volume":"14","author":"EW Esch","year":"2015","unstructured":"Esch EW, Bahinski A, Huh D. Organs-on-chips at the frontiers of drug discovery. Nat Rev Drug Discov. 2015;14(4):248\u201360.","journal-title":"Nat Rev Drug Discov"},{"issue":"8","key":"192_CR65","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1021\/acs.chemrestox.2c00196","volume":"35","author":"IV Tetko","year":"2022","unstructured":"Tetko IV, Klambauer G, Clevert DA, Shah I, Benfenati E. Artificial intelligence meets toxicology. Chem Res Toxicol. 2022;35(8):1289\u201390.","journal-title":"Chem Res Toxicol"},{"key":"192_CR66","doi-asserted-by":"publisher","first-page":"1269932","DOI":"10.3389\/frai.2023.1269932","volume":"6","author":"T Hartung","year":"2023","unstructured":"Hartung T. Artificial intelligence as the new frontier in chemical risk assessment. Front Artif Intell. 2023;6:1269932.","journal-title":"Front Artif Intell"},{"issue":"9","key":"192_CR67","doi-asserted-by":"publisher","first-page":"2628","DOI":"10.1021\/acs.jcim.3c00200","volume":"63","author":"TTT Van","year":"2023","unstructured":"Van TTT, Surya Wibowo A, Tayara H, Chong KT. Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives. J Chem Inf Model. 2023;63(9):2628\u201343.","journal-title":"J Chem Inf Model"},{"issue":"4","key":"192_CR68","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1475","volume":"10","author":"J Hemmerich","year":"2020","unstructured":"Hemmerich J, Ecker GF. In silico toxicology: From structure\u2013activity relationships towards deep learning and adverse outcome pathways. WIREs Comput Mol Sci. 2020;10(4): e1475.","journal-title":"WIREs Comput Mol Sci"},{"issue":"1","key":"192_CR69","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1093\/toxsci\/56.1.8","volume":"56","author":"JD McKinney","year":"2000","unstructured":"McKinney JD. The practice of structure activity relationships (SAR) in toxicology. Toxicol Sci. 2000;56(1):8\u201317.","journal-title":"Toxicol Sci"},{"key":"192_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopha.2023.114784","volume":"163","author":"AV Singh","year":"2023","unstructured":"Singh AV, Chandrasekar V, Paudel N, Laux P, Luch A, Gemmati D, et al. Integrative toxicogenomics: advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother. 2023;163: 114784.","journal-title":"Biomed Pharmacother"},{"issue":"1","key":"192_CR71","first-page":"20220023","volume":"5","author":"A Ismail","year":"2023","unstructured":"Ismail A, Al-Zoubi T, El Naqa I, Saeed H. The role of artificial intelligence in hastening time to recruitment in clinical trials. BJR Open. 2023;5(1):20220023.","journal-title":"BJR Open"},{"key":"192_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102111","volume":"117","author":"E Parimbelli","year":"2021","unstructured":"Parimbelli E, Wilk S, Cornet R, Sniatala P, Sniatala K, Glaser SLC, et al. A review of AI and Data Science support for cancer management. Artif Intell Med. 2021;117: 102111.","journal-title":"Artif Intell Med"},{"issue":"1","key":"192_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbcan.2021.188572","volume":"1876","author":"L Kolla","year":"2021","unstructured":"Kolla L, Gruber FK, Khalid O, Hill C, Parikh RB. The case for AI-driven cancer clinical trials\u2014the efficacy arm in silico. Biochimica et Biophysica Acta BBA Rev Cancer. 2021;1876(1): 188572.","journal-title":"Biochimica et Biophysica Acta BBA Rev Cancer"},{"issue":"5","key":"192_CR74","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1097\/JS9.0000000000000088","volume":"109","author":"C Chakraborty","year":"2023","unstructured":"Chakraborty C, Bhattacharya M, Dhama K, Agoramoorthy G. Artificial intelligence\u2013enabled clinical trials might be a faster way to perform rapid clinical trials and counter future pandemics: lessons learned from the COVID-19 period. Int J Surg. 2023;109(5):1535\u20138.","journal-title":"Int J Surg"},{"issue":"e1","key":"192_CR75","doi-asserted-by":"publisher","first-page":"e42","DOI":"10.1093\/jamia\/ocv118","volume":"23","author":"MJ Bietz","year":"2016","unstructured":"Bietz MJ, Bloss CS, Calvert S, Godino JG, Gregory J, Claffey MP, et al. Opportunities and challenges in the use of personal health data for health research. J Am Med Inform Assoc. 2016;23(e1):e42\u20138.","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"192_CR76","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1111\/cts.12884","volume":"14","author":"KB Johnson","year":"2021","unstructured":"Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, et al. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci. 2021;14(1):86\u201393.","journal-title":"Clin Transl Sci"},{"key":"192_CR77","first-page":"160","volume":"128","author":"RC Ziegelstein","year":"2017","unstructured":"Ziegelstein RC. Personomics and precision medicine. Trans Am Clin Climatol Assoc. 2017;128:160\u20138.","journal-title":"Trans Am Clin Climatol Assoc"},{"issue":"9","key":"192_CR78","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1158\/0008-5472.CAN-16-2479","volume":"77","author":"RJ Hartmaier","year":"2017","unstructured":"Hartmaier RJ, Albacker LA, Chmielecki J, Bailey M, He J, Goldberg ME, et al. High-throughput genomic profiling of adult solid tumors reveals novel insights into cancer pathogenesis. Cancer Res. 2017;77(9):2464\u201375.","journal-title":"Cancer Res"},{"issue":"1","key":"192_CR79","doi-asserted-by":"publisher","first-page":"43","DOI":"10.3892\/ijo.2020.5063","volume":"57","author":"E Trivizakis","year":"2020","unstructured":"Trivizakis E, Papadakis GZ, Souglakos I, Papanikolaou N, Koumakis L, Spandidos DA, et al. Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review). Int J Oncol. 2020;57(1):43\u201353.","journal-title":"Int J Oncol"},{"key":"192_CR80","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.compbiomed.2019.04.018","volume":"109","author":"Z Zhu","year":"2019","unstructured":"Zhu Z, Albadawy E, Saha A, Zhang J, Harowicz MR, Mazurowski MA. Deep learning for identifying radiogenomic associations in breast cancer. Comput Biol Med. 2019;109:85\u201390.","journal-title":"Comput Biol Med"},{"issue":"1","key":"192_CR81","doi-asserted-by":"publisher","first-page":"12611","DOI":"10.1038\/s41598-018-30657-6","volume":"8","author":"JE Bibault","year":"2018","unstructured":"Bibault JE, Giraud P, Housset M, Durdux C, Taieb J, Berger A, et al. Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep. 2018;8(1):12611.","journal-title":"Sci Rep"},{"issue":"3","key":"192_CR82","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/JBHI.2018.2886276","volume":"23","author":"E Trivizakis","year":"2019","unstructured":"Trivizakis E, Manikis GC, Nikiforaki K, Drevelegas K, Constantinides M, Drevelegas A, et al. Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to mri liver tumor differentiation. IEEE J Biomed Health Inform. 2019;23(3):923\u201330.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"192_CR83","doi-asserted-by":"publisher","first-page":"16444","DOI":"10.1038\/s41598-018-34753-5","volume":"8","author":"C Huang","year":"2018","unstructured":"Huang C, Clayton EA, Matyunina LV, McDonald LD, Benigno BB, Vannberg F, et al. Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy. Sci Rep. 2018;8(1):16444.","journal-title":"Sci Rep"},{"key":"192_CR84","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/B978-0-323-89925-3.00023-X","volume-title":"A handbook of artificial intelligence in drug delivery","author":"T Manzano","year":"2023","unstructured":"Manzano T, Whitford W. AI applications for multivariate control in drug manufacturing. In: A handbook of artificial intelligence in drug delivery. Elsevier; 2023. p. 55\u201382."},{"issue":"4","key":"192_CR85","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.tibtech.2022.08.007","volume":"41","author":"AS Rathore","year":"2023","unstructured":"Rathore AS, Nikita S, Thakur G, Mishra S. Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends Biotechnol. 2023;41(4):497\u2013510.","journal-title":"Trends Biotechnol"},{"key":"192_CR86","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/B978-0-323-89925-3.00015-0","volume-title":"A handbook of artificial intelligence in drug delivery","author":"S Chaudhary","year":"2023","unstructured":"Chaudhary S, Muthudoss P, Madheswaran T, Paudel A, Gaikwad V. Artificial intelligence (AI) in drug product designing, development, and manufacturing. In: A handbook of artificial intelligence in drug delivery. Elsevier; 2023. p. 395\u2013442."},{"key":"192_CR87","unstructured":"Innopharma Technology. SMARTX process automation for pharmaceutical fluid bed operations and process development. https:\/\/www.innopharmatechnology.com\/products\/smartx. Accessed 24 Aug 2024."},{"key":"192_CR88","unstructured":"Yuliya Melnik. Machine failure prediction using machine learning: why it is beneficial. 2024. https:\/\/indatalabs.com\/blog\/machine-failure-prediction-machine-learning. Accessed 24 Aug 2024"},{"key":"192_CR89","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpharm.2021.120554","volume":"602","author":"NS Arden","year":"2021","unstructured":"Arden NS, Fisher AC, Tyner K, Yu LX, Lee SL, Kopcha M. Industry 4.0 for pharmaceutical manufacturing: preparing for the smart factories of the future. Int J Pharm. 2021;602: 120554.","journal-title":"Int J Pharm"},{"key":"192_CR90","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1002\/9781119865728.ch10","volume-title":"Bioinformatics tools for pharmaceutical drug product development","author":"K Baviskar","year":"2023","unstructured":"Baviskar K, Bedse A, Raut S, Darapaneni N. Artificial intelligence and machine learning-based manufacturing and drug product marketing. In: Bioinformatics tools for pharmaceutical drug product development. Wiley; 2023. p. 197\u2013231."},{"key":"192_CR91","first-page":"1","volume":"2022","author":"YuC Artificial","year":"2022","unstructured":"Artificial YuC, Data I-B. Artificial intelligence-based drug production quality management data. Math Probl Eng. 2022;2022:1\u201314.","journal-title":"Math Probl Eng"},{"key":"192_CR92","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/B978-0-323-89925-3.00003-4","volume-title":"A handbook of artificial intelligence in drug delivery","author":"B Mesut","year":"2023","unstructured":"Mesut B, Ba\u015fkor A, Buket Aksu N. Role of artificial intelligence in quality profiling and optimization of drug products. In: A handbook of artificial intelligence in drug delivery. Elsevier; 2023. p. 35\u201354."},{"key":"192_CR93","unstructured":"Innopharma Technology. EYECON\u2082 is a direct imaging particle analyser. https:\/\/www.innopharmatechnology.com\/products\/eyecon2tm. Accessed 24 Aug 2024"},{"issue":"6","key":"192_CR94","doi-asserted-by":"publisher","first-page":"429","DOI":"10.2165\/00002018-199921060-00001","volume":"21","author":"RHB Meyboom","year":"1999","unstructured":"Meyboom RHB, Egberts AC, Gribnau FWJ, Hekster YA. Pharmacovigilance in perspective. Drug Saf. 1999;21(6):429\u201347.","journal-title":"Drug Saf"},{"issue":"6","key":"192_CR95","doi-asserted-by":"publisher","first-page":"373","DOI":"10.4103\/ijp.IJP_814_19","volume":"51","author":"K Murali","year":"2019","unstructured":"Murali K, Kaur S, Prakash A, Medhi B. Artificial intelligence in pharmacovigilance: practical utility. Indian J Pharmacol. 2019;51(6):373.","journal-title":"Indian J Pharmacol"},{"issue":"1","key":"192_CR96","doi-asserted-by":"publisher","first-page":"65","DOI":"10.4103\/0253-7613.150344","volume":"47","author":"V Tandon","year":"2015","unstructured":"Tandon V, Mahajan V, Khajuria V, Gillani Z. Under-reporting of adverse drug reactions: a challenge for pharmacovigilance in India. Indian J Pharmacol. 2015;47(1):65.","journal-title":"Indian J Pharmacol"},{"issue":"1","key":"192_CR97","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40264-013-0123-x","volume":"37","author":"GJ Dal Pan","year":"2014","unstructured":"Dal Pan GJ. Ongoing challenges in pharmacovigilance. Drug Saf. 2014;37(1):1\u20138.","journal-title":"Drug Saf"},{"issue":"10","key":"192_CR98","doi-asserted-by":"publisher","first-page":"811","DOI":"10.2165\/11316550-000000000-00000","volume":"32","author":"TJ Giezen","year":"2009","unstructured":"Giezen TJ, Mantel-Teeuwisse AK, Leufkens HGM. Pharmacovigilance of biopharmaceuticals. Drug Saf. 2009;32(10):811\u20137.","journal-title":"Drug Saf"},{"key":"192_CR99","first-page":"1","volume-title":"Encyclopedia of evidence in pharmaceutical public health and health services research in pharmacy","author":"S Chatterjee","year":"2022","unstructured":"Chatterjee S, Aparasu RR. Pharmacovigilance to inform drug safety: challenges and opportunities. In: Encyclopedia of evidence in pharmaceutical public health and health services research in pharmacy. Cham: Springer International Publishing; 2022. p. 1\u201312."},{"issue":"6","key":"192_CR100","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1007\/s40264-018-0641-7","volume":"41","author":"S Comfort","year":"2018","unstructured":"Comfort S, Perera S, Hudson Z, Dorrell D, Meireis S, Nagarajan M, et al. Sorting through the safety data haystack: using machine learning to identify individual case safety reports in social-digital media. Drug Saf. 2018;41(6):579\u201390.","journal-title":"Drug Saf"},{"key":"192_CR101","unstructured":"Tunir Das. Leveraging AI to enhance efficiency and effectiveness in adverse event management; 2023. https:\/\/www.cloudbyz.com\/resources\/pharmacovigilance\/leveraging-ai-to-enhance-efficiency-and-effectiveness-in-adverse-event-management\/. Accessed 22 Aug 2024"},{"issue":"2","key":"192_CR102","doi-asserted-by":"publisher","DOI":"10.1016\/j.hlpt.2023.100743","volume":"12","author":"A Bate","year":"2023","unstructured":"Bate A, Stegmann JU. Artificial intelligence and pharmacovigilance: what is happening, what could happen and what should happen? Health Policy Technol. 2023;12(2): 100743.","journal-title":"Health Policy Technol"},{"issue":"5","key":"192_CR103","first-page":"295","volume":"36","author":"M Salas","year":"2022","unstructured":"Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S, et al. The use of artificial intelligence in pharmacovigilance: a systematic review of the literature. Pharmaceut Med. 2022;36(5):295\u2013306.","journal-title":"Pharmaceut Med"},{"issue":"5","key":"192_CR104","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/s40264-022-01157-4","volume":"45","author":"R Ball","year":"2022","unstructured":"Ball R, Dal Pan G. \u201cArtificial Intelligence\u201d for pharmacovigilance: ready for prime time? Drug Saf. 2022;45(5):429\u201338.","journal-title":"Drug Saf"},{"issue":"4","key":"192_CR105","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/s40264-018-0746-z","volume":"42","author":"K Danysz","year":"2019","unstructured":"Danysz K, Cicirello S, Mingle E, Assuncao B, Tetarenko N, Mockute R, et al. Artificial intelligence and the future of the drug safety professional. Drug Saf. 2019;42(4):491\u20137.","journal-title":"Drug Saf"},{"key":"192_CR106","unstructured":"Genpact Inc. Genpact launches an Artificial Intelligence (AI)-based solution to usher in a new era of drug safety automation. https:\/\/media.genpact.com\/2017-06-12-Genpact-Launches-an-Artificial-Intelligence-AI-Based-Solution-to-Usher-in-a-New-Era-of-Drug-Safety-Automation. Accessed 22 Aug 2024"},{"key":"192_CR107","unstructured":"Clindata Insight Inc. Biometrics project solutions. https:\/\/www.clindatainsight.com\/biometrics-project-solutions\/. Accessed 22 Aug 2024."},{"key":"192_CR108","unstructured":"IQVIA Inc. IQVIA Vigilance PLATFORM. https:\/\/www.iqvia.com\/solutions\/safety-regulatory-compliance\/safety-and-pharmacovigilance\/iqvia-vigilance-platform. Accessed 22 Aug 2024"},{"issue":"4","key":"192_CR109","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.orhc.2014.09.002","volume":"3","author":"N Privett","year":"2014","unstructured":"Privett N, Gonsalvez D. The top ten global health supply chain issues: Perspectives from the field. Oper Res Health Care. 2014;3(4):226\u201330.","journal-title":"Oper Res Health Care"},{"issue":"1\/2","key":"192_CR110","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1504\/IJPM.2015.066292","volume":"8","author":"AMS Bravo","year":"2015","unstructured":"Bravo AMS, De CJC. Challenging times to pharmaceutical supply chains towards sustainability: a case study application. Int J Procurement Manag. 2015;8(1\/2):126.","journal-title":"Int J Procurement Manag"},{"issue":"2","key":"192_CR111","first-page":"1103","volume":"18","author":"A Moosivand","year":"2019","unstructured":"Moosivand A, Rajabzadeh Ghatari A, Rasekh HR. Supply chain challenges in pharmaceutical manufacturing companies: using qualitative system dynamics methodology. Iran J Pharm Res. 2019;18(2):1103\u201316.","journal-title":"Iran J Pharm Res"},{"issue":"6\u20137","key":"192_CR112","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1016\/j.compchemeng.2003.09.022","volume":"28","author":"N Shah","year":"2004","unstructured":"Shah N. Pharmaceutical supply chains: key issues and strategies for optimisation. Comput Chem Eng. 2004;28(6\u20137):929\u201341.","journal-title":"Comput Chem Eng"},{"issue":"3","key":"192_CR113","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1108\/IJPHM-10-2015-0050","volume":"10","author":"RK Singh","year":"2016","unstructured":"Singh RK, Kumar R, Kumar P. Strategic issues in pharmaceutical supply chains: a review. Int J Pharm Healthc Mark. 2016;10(3):234\u201357.","journal-title":"Int J Pharm Healthc Mark"},{"key":"192_CR114","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108815","volume":"175","author":"A Kumar","year":"2023","unstructured":"Kumar A, Mani V, Jain V, Gupta H, Venkatesh VG. Managing healthcare supply chain through artificial intelligence (AI): a study of critical success factors. Comput Ind Eng. 2023;175: 108815.","journal-title":"Comput Ind Eng"},{"issue":"24","key":"192_CR115","doi-asserted-by":"publisher","first-page":"7527","DOI":"10.1080\/00207543.2022.2029611","volume":"60","author":"R Sharma","year":"2022","unstructured":"Sharma R, Shishodia A, Gunasekaran A, Min H, Munim ZH. The role of artificial intelligence in supply chain management: mapping the territory. Int J Prod Res. 2022;60(24):7527\u201350.","journal-title":"Int J Prod Res"},{"key":"192_CR116","first-page":"1","volume-title":"Digital supply chain management using AI, ML and blockchain","author":"AK Gupta","year":"2022","unstructured":"Gupta AK, Awatade GV, Padole SS, Choudhari YS. Digital supply chain management using AI, ML and blockchain. Springer; 2022. p. 1\u201319."},{"issue":"2","key":"192_CR117","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1108\/IJLM-05-2021-0300","volume":"34","author":"MB Mariappan","year":"2023","unstructured":"Mariappan MB, Devi K, Venkataraman Y, Lim MK, Theivendren P. Using AI and ML to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during COVID-19 pandemic. Int J Logist Manag. 2023;34(2):390\u2013416.","journal-title":"Int J Logist Manag"},{"key":"192_CR118","unstructured":"AHIP. New study: in the midst of COVID-19 crisis, 7 out of 10 big pharma companies spent more on sales and marketing than R&D; 2021. https:\/\/www.ahip.org\/news\/articles\/new-study-in-the-midst-of-covid-19-crisis-7-out-of-10-big-pharma-companies-spent-more-on-sales-and-marketing-than-r-d."},{"key":"192_CR119","unstructured":"Stephen Wunker. How AI can revolutionize pharma sales and marketing; 2023. https:\/\/www.forbes.com\/sites\/stephenwunker\/2023\/06\/05\/how-ai-can-revolutionize-pharma-sales-and-marketing\/?sh=5bc562e26c4d."},{"issue":"2","key":"192_CR120","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1177\/2168479018772820","volume":"53","author":"L Hu","year":"2019","unstructured":"Hu L, Yu Z, Yuan Q, Hu Y, Ung COL. Opportunities and challenges of multinational pharmaceutical enterprises in transforming pharmaceutical market in China. Ther Innov Regul Sci. 2019;53(2):207\u201314.","journal-title":"Ther Innov Regul Sci"},{"issue":"4","key":"192_CR121","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1177\/07439156221112304","volume":"41","author":"C Morgan","year":"2022","unstructured":"Morgan C, Zane DM. Practitioner perspectives on key challenges in pharmaceutical marketing and future research opportunities. J Public Policy Mark. 2022;41(4):368\u201382. https:\/\/doi.org\/10.1177\/07439156221112304.","journal-title":"J Public Policy Mark"},{"issue":"2","key":"192_CR122","doi-asserted-by":"publisher","first-page":"64","DOI":"10.22270\/jddt.v4i2.771","volume":"4","author":"A Kalotra","year":"2014","unstructured":"Kalotra A. Marketing strategies of different pharmaceutical companies. Journal of Drug Delivery and Therapeutics. 2014;4(2):64\u201371.","journal-title":"Journal of Drug Delivery and Therapeutics"},{"issue":"1","key":"192_CR123","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1057\/palgrave.jmm.5040201","volume":"5","author":"M Johnston","year":"2005","unstructured":"Johnston M, Tennens M. The challenges of implementing a marketing strategy: a practitioner\u2019s view. J Med Mark. 2005;5(1):44\u201356. https:\/\/doi.org\/10.1057\/palgrave.jmm.5040201.","journal-title":"J Med Mark"},{"issue":"3","key":"192_CR124","doi-asserted-by":"publisher","first-page":"129","DOI":"10.56578\/ataiml020302","volume":"2","author":"F Farchi","year":"2023","unstructured":"Farchi F, Farchi C, Touzi B, Mabrouki C. A comparative study on AI-based algorithms for cost prediction in pharmaceutical transport logistics. Acadlore Trans AI Mach Learn. 2023;2(3):129\u201341.","journal-title":"Acadlore Trans AI Mach Learn"},{"issue":"9","key":"192_CR125","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2023.103700","volume":"28","author":"RS Patil","year":"2023","unstructured":"Patil RS, Kulkarni SB, Gaikwad VL. Artificial intelligence in pharmaceutical regulatory affairs. Drug Discov Today. 2023;28(9): 103700.","journal-title":"Drug Discov Today"},{"issue":"13","key":"192_CR126","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1108\/MD-07-2022-0909","volume":"61","author":"S Guercini","year":"2023","unstructured":"Guercini S. Marketing automation and the scope of marketers\u2019 heuristics. Manag Decis. 2023;61(13):295\u2013320.","journal-title":"Manag Decis"},{"key":"192_CR127","first-page":"373","volume-title":"Issues of using artificial intelligence in pharmaceutical retail in Russia","author":"VA Bondarenko","year":"2023","unstructured":"Bondarenko VA, Galazova SS, Kostoglodov DD, Przhedetskaya NV, Solyanskaya JV. Issues of using artificial intelligence in pharmaceutical retail in Russia. Springer; 2023. p. 373\u201380."},{"issue":"12","key":"192_CR128","first-page":"4302","volume":"10","author":"J Chen","year":"2018","unstructured":"Chen J, Luo X, Qiu H, Mackey V, Sun L, Ouyang X. Drug discovery and drug marketing with the critical roles of modern administration. Am J Transl Res. 2018;10(12):4302\u201312.","journal-title":"Am J Transl Res"},{"key":"192_CR129","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.procs.2020.06.019","volume":"173","author":"R Tiwari","year":"2020","unstructured":"Tiwari R, Srivastava S, Gera R. Investigation of artificial intelligence techniques in finance and marketing. Procedia Comput Sci. 2020;173:149\u201357.","journal-title":"Procedia Comput Sci"},{"key":"192_CR130","unstructured":"theguardian. Tesla driver dies in first fatal crash while using autopilot mode; 2016. https:\/\/www.theguardian.com\/technology\/2016\/jun\/30\/tesla-autopilot-death-self-driving-car-elon-musk."},{"issue":"5","key":"192_CR131","doi-asserted-by":"publisher","first-page":"358","DOI":"10.4103\/ijd.IJD_419_20","volume":"65","author":"T Basu","year":"2020","unstructured":"Basu T, Engel-Wolf S, Menzer O. The ethics of machine learning in medical sciences: where do we stand today? Indian J Dermatol. 2020;65(5):358.","journal-title":"Indian J Dermatol"},{"key":"192_CR132","doi-asserted-by":"crossref","unstructured":"Shimao H, Khern-am-nuai W, Kannan K, Cohen MC. Strategic best response fairness in fair machine learning. In: Proceedings of the 2022 AAAI\/ACM conference on AI, ethics, and society; 2022. p. 664.","DOI":"10.1145\/3514094.3534194"},{"key":"192_CR133","doi-asserted-by":"crossref","unstructured":"Kleinberg J. Inherent trade-offs in algorithmic fairness. In: Abstracts of the 2018 ACM international conference on measurement and modeling of computer systems; 2018. p. 40.","DOI":"10.1145\/3219617.3219634"},{"issue":"6","key":"192_CR134","doi-asserted-by":"publisher","first-page":"891","DOI":"10.3390\/ph16060891","volume":"16","author":"A Blanco-Gonz\u00e1lez","year":"2023","unstructured":"Blanco-Gonz\u00e1lez A, Cabez\u00f3n A, Seco-Gonz\u00e1lez A, Conde-Torres D, Antelo-Riveiro P, Pi\u00f1eiro \u00c1, et al. The role of AI in drug discovery: challenges, opportunities, and strategies. Pharmaceuticals. 2023;16(6):891.","journal-title":"Pharmaceuticals"},{"key":"192_CR135","volume-title":"Handbook on securing cyber-physical critical infrastructure","author":"SK Das","year":"2012","unstructured":"Das SK, Kant K, Zhang N. Handbook on securing cyber-physical critical infrastructure. Elsevier; 2012."},{"issue":"1","key":"192_CR136","doi-asserted-by":"publisher","first-page":"271","DOI":"10.3390\/ijerph18010271","volume":"18","author":"D Lee","year":"2021","unstructured":"Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Public Health. 2021;18(1):271.","journal-title":"Int J Environ Res Public Health"},{"key":"192_CR137","unstructured":"The European Parliament and the Council of the European Union. Document 32024R1689. https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=OJ:L_202401689. Accessed 23 Aug 2024."},{"key":"192_CR138","unstructured":"European Medicines Agency. Artificial intelligence workplan to guide use of AI in medicines regulation; 2023. https:\/\/www.ema.europa.eu\/en\/news\/artificial-intelligence-workplan-guide-use-ai-medicines-regulation. Accessed 23 Aug 2024"},{"key":"192_CR139","unstructured":"International Organization for Standardization (ISO). ISO\/IEC 42001:2023. https:\/\/www.iso.org\/standard\/81230.html. Accessed 23 Aug 2024."},{"key":"192_CR140","unstructured":"U.S. Food and Drug Administration. Artificial intelligence & medical products: how CBER, CDER, CDRH, and OCP are working together. https:\/\/www.fda.gov\/media\/177030\/download. Accessed 23 Aug 2024."},{"issue":"9","key":"192_CR141","first-page":"32","volume":"47","author":"AC Fisher","year":"2023","unstructured":"Fisher AC. The future is the present: artificial intelligence in pharmaceutical manufacturing. Pharm Technol. 2023;47(9):32\u20134.","journal-title":"Pharm Technol"},{"issue":"2","key":"192_CR142","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1134\/S1068162023020139","volume":"49","author":"VS Kulkarni","year":"2023","unstructured":"Kulkarni VS, Alagarsamy V, Solomon VR, Jose PA, Murugesan S. Drug repurposing: an effective tool in modern drug discovery. Russ J Bioorg Chem. 2023;49(2):157\u201366.","journal-title":"Russ J Bioorg Chem"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00192-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-024-00192-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00192-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T18:04:26Z","timestamp":1731607466000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-024-00192-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":142,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["192"],"URL":"https:\/\/doi.org\/10.1007\/s44163-024-00192-7","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,14]]},"assertion":[{"value":"29 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Studies of this nature do not necessitate ethical approval or consent to participate.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"82"}}