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Current Artificial Intelligence

Editor-in-Chief

ISSN (Print): 2950-3752
ISSN (Online): 2950-3760

Review Article

Artificial Intelligence in Pharmaceutical Industry: Revolutionizing Drug Development and Delivery

Author(s): Pankaj Bhatt, Suruchi Singh*, Vipin Kumar, Kandasamy Nagarajan, Shardandu Kumar Mishra, Praveen Kumar Dixit, Vinay Kumar and Sanjeev Kumar

Volume 2, 2024

Published on: 05 December, 2023

Page: [17 - 33] Pages: 17

DOI: 10.2174/0129503752250813231124092946

Price: $65

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Abstract

Artificial Intelligence (AI) has ushered in a profound revolution within the pharmaceutical sector, effectively streamlining the processes of drug development and delivery. The application of AI-driven tools and methodologies, including machine learning and natural language processing, in the realm of pharmaceutical research and development has yielded recent breakthroughs. This accelerated the drug discovery process by meticulously scrutinizing copious data and pinpointing potential drug targets, as expounded upon in this comprehensive review. Furthermore, AI has found utility in optimizing clinical trials, thereby refining trial designs and cost-effectiveness and bolstering patient safety. Notably, AI-based strategies are being harnessed to enhance drug delivery, fostering the creation of intelligent drug delivery systems engineered to target specific cells or organs. This results in heightened efficacy and a concomitant reduction in undesirable side effects. This review also delves into the potential biases residing within AI algorithms and the challenges associated with data quality when integrating AI into the pharmaceutical sphere. The findings of this study underscore the immense potential of artificial intelligence in reshaping the pharmaceutical industry, thereby enhancing the quality of life for patients worldwide.

Keywords: Machine learning adverse event prediction, molecule design, drug development, personalized medicine, artificial intelligence, cost-effectiveness.

[1]
Patel, V.; Shah, M. Artificial intelligence and machine learning in drug discovery and development. Intell. Med., 2022, 2(3), 134-140.
[http://dx.doi.org/10.1016/j.imed.2021.10.001]
[2]
Henstock, P.V. Artificial intelligence for pharma: Time for internal investment. Trends Pharmacol. Sci., 2019, 40(8), 543-546.
[http://dx.doi.org/10.1016/j.tips.2019.05.003] [PMID: 31204059]
[3]
Al-Safarini, M.Y.; El-Sayed, H.H. The role of artificial intelligence in revealing the results of the interaction of biological materials with each other or with chemicals. Mater. Today Proc., 2021, 45, 4954-4959.
[http://dx.doi.org/10.1016/j.matpr.2021.01.387]
[4]
Mak, K.K.; Pichika, M.R. Artificial intelligence in drug development: Present status and future prospects. Drug Discov. Today, 2019, 24(3), 773-780.
[http://dx.doi.org/10.1016/j.drudis.2018.11.014] [PMID: 30472429]
[5]
Weng, Y.; Lin, C.; Zeng, X.; Liang, Y. Drug target interaction prediction using multi-task learning and co-attention. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019, pp. 528-533.
[http://dx.doi.org/10.1109/BIBM47256.2019.8983254]
[6]
Margulis, E.; Dagan-Wiener, A.; Ives, R.S.; Jaffari, S.; Siems, K.; Niv, M.Y. Intense bitterness of molecules: Machine learning for expediting drug discovery. Comput. Struct. Biotechnol. J., 2021, 19, 568-576.
[http://dx.doi.org/10.1016/j.csbj.2020.12.030] [PMID: 33510862]
[7]
Bender, A.; Cortes-Ciriano, I. Artificial intelligence in drug discovery: What is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov. Today, 2021, 26(4), 1040-1052.
[http://dx.doi.org/10.1016/j.drudis.2020.11.037] [PMID: 33508423]
[8]
Decker, S.; Sausville, E.A. Drug discovery. In: Principles of Clinical Pharmacology; Elsevier, 2007; pp. 439-447.
[http://dx.doi.org/10.1016/B978-012369417-1/50068-7]
[9]
McLean, L. Drug development. In: Rheumatology; Elsevier, 2015; pp. 395-400.
[http://dx.doi.org/10.1016/B978-0-323-09138-1.00049-8]
[10]
Paul, S.M.; Mytelka, D.S.; Dunwiddie, C.T.; Persinger, C.C.; Munos, B.H.; Lindborg, S.R.; Schacht, A.L. How to improve R&D productivity: The pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov., 2010, 9(3), 203-214.
[http://dx.doi.org/10.1038/nrd3078] [PMID: 20168317]
[11]
Wang, L.; Ding, J.; Pan, L.; Cao, D.; Jiang, H.; Ding, X. Artificial intelligence facilitates drug design in the big data era. Chemom. Intell. Lab. Syst., 2019, 194, 103850.
[http://dx.doi.org/10.1016/j.chemolab.2019.103850]
[12]
Polykovskiy, D.; Zhebrak, A.; Vetrov, D.; Ivanenkov, Y.; Aladinskiy, V.; Mamoshina, P.; Bozdaganyan, M.; Aliper, A.; Zhavoronkov, A.; Kadurin, A. Entangled conditional adversarial autoencoder for de novo drug discovery. Mol. Pharm., 2018, 15(10), 4398-4405.
[http://dx.doi.org/10.1021/acs.molpharmaceut.8b00839] [PMID: 30180591]
[13]
Kalra, D.; Stroetmann, V.; Sundgren, M.; Dupont, D.; Schlünder, I.; Thienpont, G.; Coorevits, P.; De Moor, G. The EUROPEAN INSTITUTE FOR INNOVATION THROUGH HEALTH DATA. Learn. Health Syst., 2017, 1(1), e10008.
[http://dx.doi.org/10.1002/lrh2.10008] [PMID: 31245550]
[14]
Kalyane, D. Artificial intelligence in the pharmaceutical sector: Current scene and future prospect. In: The Future of Pharmaceutical Product Development and Research; Elsevier, 2020; pp. 73-107.
[http://dx.doi.org/10.1016/B978-0-12-814455-8.00003-7]
[15]
Paul, D.; Sanap, G.; Shenoy, S.; Kalyane, D.; Kalia, K.; Tekade, R.K. Artificial intelligence in drug discovery and development. Drug Discov. Today, 2021, 26(1), 80-93.
[http://dx.doi.org/10.1016/j.drudis.2020.10.010] [PMID: 33099022]
[16]
Lusci, A.; Pollastri, G.; Baldi, P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model., 2013, 53(7), 1563-1575.
[http://dx.doi.org/10.1021/ci400187y] [PMID: 23795551]
[17]
Zhou, Y.; Wang, F.; Tang, J.; Nussinov, R.; Cheng, F. Artificial intelligence in COVID-19 drug repurposing. Lancet Digit. Health, 2020, 2(12), e667-e676.
[http://dx.doi.org/10.1016/S2589-7500(20)30192-8] [PMID: 32984792]
[18]
Tanoli, Z.; Vähä-Koskela, M.; Aittokallio, T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opin. Drug Discov., 2021, 16(9), 977-989.
[http://dx.doi.org/10.1080/17460441.2021.1883585] [PMID: 33543671]
[19]
Liebman, M. The role of artificial intelligence in drug discovery and development. Chem. Int., 2022, 44(1), 16-19.
[http://dx.doi.org/10.1515/ci-2022-0105]
[20]
Castro, V.M.; Minnier, J.; Murphy, S.N.; Kohane, I.; Churchill, S.E.; Gainer, V.; Cai, T.; Hoffnagle, A.G.; Dai, Y.; Block, S.; Weill, S.R.; Nadal-Vicens, M.; Pollastri, A.R.; Rosenquist, J.N.; Goryachev, S.; Ongur, D.; Sklar, P.; Perlis, R.H.; Smoller, J.W.; Smoller, J.W.; Perlis, R.H.; Lee, P.H.; Castro, V.M.; Hoffnagle, A.G.; Sklar, P.; Stahl, E.A.; Purcell, S.M.; Ruderfer, D.M.; Charney, A.W.; Roussos, P.; Pato, C.; Pato, M.; Medeiros, H.; Sobel, J.; Craddock, N.; Jones, I.; Forty, L.; DiFlorio, A.; Green, E.; Jones, L.; Dunjewski, K.; Landén, M.; Hultman, C.; Juréus, A.; Bergen, S.; Svantesson, O.; McCarroll, S.; Moran, J.; Smoller, J.W.; Chambert, K.; Belliveau, R.A., Jr Validation of electronic health record phenotyping of bipolar disorder cases and controls. Am. J. Psychiatry, 2015, 172(4), 363-372.
[http://dx.doi.org/10.1176/appi.ajp.2014.14030423] [PMID: 25827034]
[21]
Jiménez-Luna, J.; Grisoni, F.; Weskamp, N.; Schneider, G. Artificial intelligence in drug discovery: Recent advances and future perspectives. Expert Opin. Drug Discov., 2021, 16(9), 949-959.
[http://dx.doi.org/10.1080/17460441.2021.1909567] [PMID: 33779453]
[22]
Schork, N.J. Artificial intelligence and personalized medicine. Cancer Treat. Res., 2019, 178, 265-283.
[http://dx.doi.org/10.1007/978-3-030-16391-4_11] [PMID: 31209850]
[23]
Savage, N. Tapping into the drug discovery potential of AI. Biopharma Dealmakers, 2021. (May)
[http://dx.doi.org/10.1038/d43747-021-00045-7]
[24]
Chen, Z.; Liu, X.; Hogan, W.; Shenkman, E.; Bian, J. Applications of artificial intelligence in drug development using real-world data. Drug Discov. Today, 2021, 26(5), 1256-1264.
[http://dx.doi.org/10.1016/j.drudis.2020.12.013] [PMID: 33358699]
[25]
Schroedl, S. Current methods and challenges for deep learning in drug discovery. Drug Discov. Today. Technol., 2019, 32-33, 9-17.
[http://dx.doi.org/10.1016/j.ddtec.2020.07.003] [PMID: 33386100]
[26]
Shen, J.; Nicolaou, C.A. Molecular property prediction: Recent trends in the era of artificial intelligence. Drug Discov. Today. Technol., 2019, 32-33, 29-36.
[http://dx.doi.org/10.1016/j.ddtec.2020.05.001] [PMID: 33386091]
[27]
Gurung, A.B.; Ali, M.A.; Lee, J.; Farah, M.A.; Al-Anazi, K.M. An updated review of computer-aided drug design and its application to COVID-19. BioMed Res. Int., 2021, 2021, 1-18.
[http://dx.doi.org/10.1155/2021/8853056] [PMID: 34258282]
[28]
Masys, D.R.; Jarvik, G.P.; Abernethy, N.F.; Anderson, N.R.; Papanicolaou, G.J.; Paltoo, D.N.; Hoffman, M.A.; Kohane, I.S.; Levy, H.P. Technical desiderata for the integration of genomic data into Electronic Health Records. J. Biomed. Inform., 2012, 45(3), 419-422.
[http://dx.doi.org/10.1016/j.jbi.2011.12.005] [PMID: 22223081]
[29]
Faraone, S.V.; Blehar, M.; Pepple, J.; Moldin, S.O.; Norton, J.; Nurnberger, J.I.; Malaspina, D.; Kaufmann, C.A.; Reich, T.; Cloninger, C.R.; DePaulo, J.R.; Berg, K.; Gershon, E.S.; Kirch, D.G.; Tsuang, M.T. Diagnostic accuracy and confusability analyses: An application to the Diagnostic Interview for Genetic Studies. Psychol. Med., 1996, 26(2), 401-410.
[http://dx.doi.org/10.1017/S0033291700034796] [PMID: 8685296]
[30]
Rodrigues, T. The good, the bad, and the ugly in chemical and biological data for machine learning. Drug Discov. Today. Technol., 2019, 32-33, 3-8.
[http://dx.doi.org/10.1016/j.ddtec.2020.07.001] [PMID: 33386092]
[31]
Lawson, C.E.; Martí, J.M.; Radivojevic, T.; Jonnalagadda, S.V.R.; Gentz, R.; Hillson, N.J.; Peisert, S.; Kim, J.; Simmons, B.A.; Petzold, C.J.; Singer, S.W.; Mukhopadhyay, A.; Tanjore, D.; Dunn, J.G.; Garcia Martin, H. Machine learning for metabolic engineering: A review. Metab. Eng., 2021, 63, 34-60.
[http://dx.doi.org/10.1016/j.ymben.2020.10.005] [PMID: 33221420]
[32]
Ayres, L.B.; Gomez, F.J.V.; Linton, J.R.; Silva, M.F.; Garcia, C.D. Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Anal. Chim. Acta, 2021, 1161, 338403.
[http://dx.doi.org/10.1016/j.aca.2021.338403] [PMID: 33896558]
[33]
Lee, K.; Yang, A.; Lin, Y.C.; Reker, D.; Bernardes, G.J.L.; Rodrigues, T. Combating small-molecule aggregation with machine learning. Cell Rep. Phys. Sci., 2021, 2(9), 100573.
[http://dx.doi.org/10.1016/j.xcrp.2021.100573]
[34]
Nielsen, M.K.; Ahneman, D.T.; Riera, O.; Doyle, A.G. Deoxyfluorination with sulfonyl fluorides: Navigating reaction space with machine learning. J. Am. Chem. Soc., 2018, 140(15), 5004-5008.
[http://dx.doi.org/10.1021/jacs.8b01523] [PMID: 29584953]
[35]
Ahneman, D.T.; Estrada, J.G.; Lin, S.; Dreher, S.D.; Doyle, A.G. Predicting reaction performance in C–N cross-coupling using machine learning. Science, 2018, 360(6385)
[http://dx.doi.org/10.1126/science.aar5169]
[36]
Osakwe, O. The significance of discovery screening and structure optimization studies. In: Social Aspects of Drug Discovery, Development and Commercialization; Elsevier, 2016; pp. 109-128.
[http://dx.doi.org/10.1016/B978-0-12-802220-7.00005-3]
[37]
Sofi, M.Y.; Shafi, A.; Masoodi, K.Z. Introduction to computer-aided drug design. In: Bioinformatics for Everyone; Elsevier, 2022; pp. 215-229.
[http://dx.doi.org/10.1016/B978-0-323-91128-3.00002-1]
[38]
Acharya, C.; Coop, A.; Polli, J.E.; Mackerell, A.D., Jr Recent advances in ligand-based drug design: Relevance and utility of the conformationally sampled pharmacophore approach. Curr. Computeraided Drug Des., 2011, 7(1), 10-22.
[http://dx.doi.org/10.2174/157340911793743547] [PMID: 20807187]
[39]
Tiwari, A.; Singh, S. Computational approaches in drug designing. In: Bioinformatics; Elsevier, 2022; pp. 207-217.
[http://dx.doi.org/10.1016/B978-0-323-89775-4.00010-9]
[40]
Woo, M. An AI boost for clinical trials. Nature, 2019, 573(7775), S100-S102.
[http://dx.doi.org/10.1038/d41586-019-02871-3] [PMID: 31554996]
[41]
Sharpless, N.E.; Kerlavage, A.R. The potential of AI in cancer care and research. Biochim. Biophys. Acta Rev. Cancer, 2021, 1876(1), 188573.
[http://dx.doi.org/10.1016/j.bbcan.2021.188573] [PMID: 34052390]
[42]
Harrer, S.; Shah, P.; Antony, B.; Hu, J. Artificial intelligence for clinical trial design. Trends Pharmacol. Sci., 2019, 40(8), 577-591.
[http://dx.doi.org/10.1016/j.tips.2019.05.005] [PMID: 31326235]
[43]
Hay, M.; Thomas, D.W.; Craighead, J.L.; Economides, C.; Rosenthal, J. Clinical development success rates for investigational drugs. Nat. Biotechnol., 2014, 32(1), 40-51.
[http://dx.doi.org/10.1038/nbt.2786] [PMID: 24406927]
[44]
Romero, K.; Ito, K.; Rogers, J.A.; Polhamus, D.; Qiu, R.; Stephenson, D.; Mohs, R.; Lalonde, R.; Sinha, V.; Wang, Y.; Brown, D.; Isaac, M.; Vamvakas, S.; Hemmings, R.; Pani, L.; Bain, L.J.; Corrigan, B. The future is now: Model-based clinical trial design for Alzheimer’s disease. Clin. Pharmacol. Ther., 2015, 97(3), 210-214.
[http://dx.doi.org/10.1002/cpt.16] [PMID: 25669145]
[45]
Wong, C.H.; Siah, K.W.; Lo, A.W. Estimation of clinical trial success rates and related parameters. Biostatistics, 2019, 20(2), 273-286.
[http://dx.doi.org/10.1093/biostatistics/kxx069] [PMID: 29394327]
[46]
Fogel, D.B. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemp. Clin. Trials Commun., 2018, 11, 156-164.
[http://dx.doi.org/10.1016/j.conctc.2018.08.001] [PMID: 30112460]
[47]
Che, D.; Liu, Q.; Rasheed, K.; Tao, X. Decision tree and ensemble learning algorithms with their applications in bioinformatics. Adv. Exp. Med. Biol., 2011, 696, 191-199.
[http://dx.doi.org/10.1007/978-1-4419-7046-6_19] [PMID: 21431559]
[48]
Choi, D.J.; Park, J.J.; Ali, T.; Lee, S. Artificial intelligence for the diagnosis of heart failure. NPJ Digit. Med., 2020, 3(1), 1-6.
[http://dx.doi.org/10.1038/s41746-020-0261-3]
[49]
Wang, J.; Yu, H.; Hua, Q.; Jing, S.; Liu, Z.; Peng, X.; Cao, C.; Luo, Y. A descriptive study of random forest algorithm for predicting COVID-19 patients outcome. PeerJ, 2020, 8, e9945.
[http://dx.doi.org/10.7717/peerj.9945] [PMID: 32974109]
[50]
Mosquera, O.A. Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: An analysis of the Spanish Myeloma Group. Blood Cancer J., 2022, 12(4), 1-9.
[http://dx.doi.org/10.1038/s41408-022-00647-z]
[51]
Tu, Z.; Tian, T.; Chen, Q.; Li, C. Overall survival analyses following adjuvant chemotherapy or nonadjuvant chemotherapy in patients with stage IB non-small-cell lung cancer. J. Oncol., 2021, 2021, 1-10.
[http://dx.doi.org/10.1155/2021/8052752] [PMID: 34335761]
[52]
Tan, X.; Yu, F.; Zhao, X. Support vector machine algorithm for artificial intelligence optimization. Cluster Comput., 2019, 22(S6), 15015-15021.
[http://dx.doi.org/10.1007/s10586-018-2490-7]
[53]
Chang, C.H.; Lin, C.H.; Lane, H.Y. Machine learning and novel biomarkers for the diagnosis of Alzheimer’s Disease. Int. J. Mol. Sci., 2021, 22(5), 2761.
[http://dx.doi.org/10.3390/ijms22052761] [PMID: 33803217]
[54]
McCoy, C. Understanding the use of composite endpoints in clinical trials. West. J. Emerg. Med., 2018, 19(4), 631-634.
[http://dx.doi.org/10.5811/westjem.2018.4.38383] [PMID: 30013696]
[55]
Ballarini, N.M.; Rosenkranz, G.K.; Jaki, T.; König, F.; Posch, M. Subgroup identification in clinical trials via the predicted individual treatment effect. PLoS One, 2018, 13(10), e0205971.
[http://dx.doi.org/10.1371/journal.pone.0205971] [PMID: 30335831]
[56]
Rajpurkar, P.; Chen, E.; Banerjee, O.; Topol, E.J. AI in health and medicine. Nat. Med., 2022, 28(1), 31-38.
[http://dx.doi.org/10.1038/s41591-021-01614-0] [PMID: 35058619]
[57]
Johannet, P.; Coudray, N.; Donnelly, D.M.; Jour, G.; Illa-Bochaca, I.; Xia, Y.; Johnson, D.B.; Wheless, L.; Patrinely, J.R.; Nomikou, S.; Rimm, D.L.; Pavlick, A.C.; Weber, J.S.; Zhong, J.; Tsirigos, A.; Osman, I. Using machine learning algorithms to predict immunotherapy response in patients with advanced melanoma. Clin. Cancer Res., 2021, 27(1), 131-140.
[http://dx.doi.org/10.1158/1078-0432.CCR-20-2415] [PMID: 33208341]
[58]
O’Neil, J.; Benita, Y.; Feldman, I.; Chenard, M.; Roberts, B.; Liu, Y.; Li, J.; Kral, A.; Lejnine, S.; Loboda, A.; Arthur, W.; Cristescu, R.; Haines, B.B.; Winter, C.; Zhang, T.; Bloecher, A.; Shumway, S.D. An unbiased oncology compound screen to identify novel combination strategies. Mol. Cancer Ther., 2016, 15(6), 1155-1162.
[http://dx.doi.org/10.1158/1535-7163.MCT-15-0843] [PMID: 26983881]
[59]
Li, T.; Le, W. Biomarkers for Parkinson’s Disease: How good are they? Neurosci. Bull., 2020, 36(2), 183-194.
[http://dx.doi.org/10.1007/s12264-019-00433-1] [PMID: 31646434]
[60]
Ageron, B.; Benzidia, S.; Bourlakis, M. Healthcare logistics and supply chain – issues and future challenges. Supply Chain Forum. Int. J., 2018, 19(1), 1-3.
[http://dx.doi.org/10.1080/16258312.2018.1433353]
[61]
Ash, J.S.; Berg, M.; Coiera, E. Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. J. Am. Med. Inform. Assoc., 2003, 11(2), 104-112.
[http://dx.doi.org/10.1197/jamia.M1471] [PMID: 14633936]
[62]
Bentahar, O.; Benzidia, S.; Fabbri, R. Traceability project of a blood supply chain. Supply Chain Forum: Int J., 2016, 17(1), 15-25.
[http://dx.doi.org/10.1080/16258312.2016.1177916]
[63]
What is artificial intelligence in healthcare? Available from: https://www.ibm.com/topics/artificial-intelligence-healthcare (Accessed on: Mar. 06, 2023).
[64]
Sieberg, C.B.; Smith, A.; White, M.; Manganella, J.; Sethna, N.; Logan, D.E. Changes in maternal and paternal pain-related attitudes, behaviors, and perceptions across pediatric pain rehabilitation treatment: A multilevel modeling approach. J. Pediatr. Psychol., 2016, 42(1), jsw046.
[http://dx.doi.org/10.1093/jpepsy/jsw046] [PMID: 28175324]
[65]
Tzelios, C.; Nathavitharana, R.R. Can AI technologies close the diagnostic gap in tuberculosis? Lancet Digit. Health, 2021, 3(9), e535-e536.
[http://dx.doi.org/10.1016/S2589-7500(21)00142-4] [PMID: 34446263]
[66]
What is artificial intelligence in medicine? Available from: https://www.ibm.com/topics/artificial-intelligence-medicine (Accessed on: Mar. 06, 2023).
[67]
Artificial Intelligence for Drug Discovery. Available from: https://www.atomwise.com/ (Accessed on: Mar. 07, 2023).
[68]
Muhammad, W.; Hart, G.R.; Nartowt, B.; Farrell, J.J.; Johung, K.; Liang, Y.; Deng, J. Pancreatic cancer prediction through an artificial neural network. Front. Artif. Intell., 2019, 2, 2.
[http://dx.doi.org/10.3389/frai.2019.00002] [PMID: 33733091]
[69]
Bajwa, J.; Munir, U.; Nori, A.; Williams, B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthc. J., 2021, 8(2), e188-e194.
[http://dx.doi.org/10.7861/fhj.2021-0095] [PMID: 34286183]
[70]
Dara, S.; Dhamercherla, S.; Jadav, S.S.; Babu, C.H.M.; Ahsan, M.J. Machine learning in drug discovery: A review. Artif. Intell. Rev., 2022, 55(3), 1947-1999.
[http://dx.doi.org/10.1007/s10462-021-10058-4] [PMID: 34393317]
[71]
Mazzocato, P.; Savage, C.; Brommels, M.; Aronsson, H.; Thor, J. Lean thinking in healthcare: A realist review of the literature. BMJ Qual. Saf., 2010, 19(5), 376-382.
[http://dx.doi.org/10.1136/qshc.2009.037986] [PMID: 20724397]
[72]
Walczak, S. The role of artificial intelligence in clinical decision support systems and a classification framework. Int. J. Comput. Clin. Pract., 2018, 3(2), 31-47.
[http://dx.doi.org/10.4018/IJCCP.2018070103]
[73]
Motulsky, A.; Nikiema, J.N.; Bosson-Rieutort, D. Artificial intelligence and medication management. Multiple Perspectives on Artificial Intelligence in Healthcare, 2021, 91-101.
[http://dx.doi.org/10.1007/978-3-030-67303-1_8]
[74]
Cirillo, D.; Catuara-Solarz, S.; Morey, C.; Guney, E.; Subirats, L.; Mellino, S.; Gigante, A.; Valencia, A.; Rementeria, M.J.; Chadha, A.S.; Mavridis, N. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. NPJ Digit. Med., 2020, 3(1), 81.
[http://dx.doi.org/10.1038/s41746-020-0288-5] [PMID: 32529043]
[75]
Romm, E.L.; Tsigelny, I.F. Artificial Intelligence in Drug Treatment. Annu. Rev. Pharmacol. Toxicol., 2020, 60(1), 353-369.
[http://dx.doi.org/10.1146/annurev-pharmtox-010919-023746] [PMID: 31348869]
[76]
Kandoth, C.; McLellan, M.D.; Vandin, F.; Ye, K.; Niu, B.; Lu, C.; Xie, M.; Zhang, Q.; McMichael, J.F.; Wyczalkowski, M.A.; Leiserson, M.D.M.; Miller, C.A.; Welch, J.S.; Walter, M.J.; Wendl, M.C.; Ley, T.J.; Wilson, R.K.; Raphael, B.J.; Ding, L. Mutational landscape and significance across 12 major cancer types. Nature, 2013, 502(7471), 333-339.
[http://dx.doi.org/10.1038/nature12634] [PMID: 24132290]
[77]
Case Studies. Available from: https://www.ibm.com/case-studies/search (Accessed on: Feb. 28, 2023).
[78]
IBM Watson Health | AI Healthcare Solutions | IBM. Available from: https://www.ibm.com/watson-health(Accessed on: Mar. 01, 2023).
[79]
Article, R. Recent apporaches in bilayered technology: Review. Int. J. Pharm. Sci. Res., 2012, 3(12), 4681-4688.
[http://dx.doi.org/10.13040/IJPSR.0975-8232.3(12).4681-88]
[80]
Diagnostic imaging solutions | IBM. Available from: https://www.ibm.com/watson-health/solutions/diagnostic-imaging
[81]
Mishima, H.; Suzuki, H.; Doi, M.; Miyazaki, M.; Watanabe, S.; Matsumoto, T.; Morifuji, K.; Moriuchi, H.; Yoshiura, K.; Kondoh, T.; Kosaki, K. Evaluation of Face2Gene using facial images of patients with congenital dysmorphic syndromes recruited in Japan. J. Hum. Genet., 2019, 64(8), 789-794.
[http://dx.doi.org/10.1038/s10038-019-0619-z] [PMID: 31138847]
[82]
Zarate, Y.A.; Bosanko, K.A.; Gripp, K.W. Using facial analysis technology in a typical genetic clinic: Experience from 30 individuals from a single institution. J. Hum. Genet., 2019, 64(12), 1243-1245.
[http://dx.doi.org/10.1038/s10038-019-0673-6] [PMID: 31551534]
[83]
Gurovich, Y.; Hanani, Y.; Bar, O.; Nadav, G.; Fleischer, N.; Gelbman, D.; Basel-Salmon, L.; Krawitz, P.M.; Kamphausen, S.B.; Zenker, M.; Bird, L.M.; Gripp, K.W. Identifying facial phenotypes of genetic disorders using deep learning. Nat. Med., 2019, 25(1), 60-64.
[http://dx.doi.org/10.1038/s41591-018-0279-0] [PMID: 30617323]
[84]
AI-enabled drug discovery. Available from: https://www.benevolent.com/(Accessed on: Feb. 28, 2023).
[85]
Sahu, A.; Mishra, J.; Kushwaha, N. Artificial intelligence (AI) in drugs and pharmaceuticals. Comb. Chem. High Throughput Screen., 2022, 25(11), 1818-1837.
[http://dx.doi.org/10.2174/1386207325666211207153943] [PMID: 34875986]
[86]
BenevolentAI Achieves Further Milestones In AI-Enabled Target Identification Collaboration With AstraZeneca. Available from: https://www.benevolent.com/news/benevolentai-achieves-further-milestones-in-ai-enabled-target-identification-collaboration-with-astrazeneca(Accessed on: Mar. 01, 2023).
[87]
ALWAYS ON AI. Connecting all points of care. Available from: https://www.aidoc.com/(Accessed on: Mar. 02, 2023).
[88]
Radiologists’ Go-To AI Solution. Available from: https://www.aidoc.com/radiology-ai/ (Accessed on: Mar. 02, 2023).
[89]
IBM - Announcements. Available from: https://newsroom.ibm.com/announcements?item=122916 (Accessed on: Mar. 02, 2023).
[90]
Medtronic and IBM Release Sweet Insights on Sugar.IQ. Available from: https://www.mddionline.com/digital-health/medtronic-and-ibm-release-sweet-insights-sugariq (Accessed on: Mar. 02, 2023).
[91]
Drug The Undruggable. Available from: https://www.atomwise.com/drug-the-undruggable/ (Accessed on: Mar. 02, 2023).
[92]
Q&A: Atomwise’s journey from discoverer to developer., Available from: https://www.pharmaceutical-technology.com/features/atomwise-qa/ (Accessed on: Mar. 02, 2023).
[93]
Atomwise, which uses AI to improve drug discovery, raises $45M Series A. Available from: https://techcrunch.com/2018/03/07/atomwise-which-uses-ai-to-improve-drug-discovery-raises-45m-series-a/ (Accessed on: Mar. 02, 2023).
[94]
Artificial intelligence for every step of pharmaceutical research and development. Available from: https://insilico.com/ (Accessed on: Mar. 02, 2023).
[95]
Pun, F.W.; Liu, B.H.M.; Long, X.; Leung, H.W.; Leung, G.H.D.; Mewborne, Q.T.; Gao, J.; Shneyderman, A.; Ozerov, I.V.; Wang, J.; Ren, F.; Aliper, A.; Bischof, E.; Izumchenko, E.; Guan, X.; Zhang, K.; Lu, B.; Rothstein, J.D.; Cudkowicz, M.E.; Zhavoronkov, A. Identification of therapeutic targets for amyotrophic lateral sclerosis using pandaOmics – An AI-enabled biological target discovery platform. Front. Aging Neurosci., 2022, 14, 914017.
[http://dx.doi.org/10.3389/fnagi.2022.914017] [PMID: 35837482]
[96]
Insilico Medicine begins first human trial of its AI-designed drug for pulmonary fibrosis. Available from: https://www.fiercebiotech.com/medtech/insilico-medicine-begins-first-human-trial-its-ai-designed-drug-for-pulmonary-fibrosis (Accessed on: Mar. 02, 2023).
[97]
Sanofi taps into Insilico Medicine’s AI platform in deal worth up to $1.2bn. Available from: https://www.biopharma-reporter.com/Article/2022/11/10/Sanofi-taps-into-Insilico-Medicine-s-AI-platform-in-deal-worth-up-to-1.2bn (Accessed on: Mar. 02, 2023).
[98]
Cloud Home. Available from: https://www.cloudpharmaceuticals.com/(Accessed on: Feb. 28, 2023).
[99]
Cloud Pharmaceuticals - Overview, News & Competitors | Zoom-Info.com Available from: https://www.zoominfo.com/c/cloud-pharmaceuticals-inc/363127363 (Accessed on: Mar. 02, 2023).
[100]
Cloud Pharmaceuticals forms Drug Design Collaboration with GSK. Available from: https://www.businesswire.com/news/home/20180530006184/en/Cloud-Pharmaceuticals-forms-Drug-Design-Collaboration-with-GSK (Accessed on: Mar. 02, 2023).
[101]
Blasiak, A.; Khong, J.; Kee, T. CURATE.AI: Optimizing personalized medicine with artificial intelligence. SLAS Technol., 2020, 25(2), 95-105.
[http://dx.doi.org/10.1177/2472630319890316] [PMID: 31771394]
[102]
AI drug miner XtalPi strikes gold with $400M infusion, its 2nd VC megaround in a year. Available from: https://www.fiercebiotech.com/medtech/ai-drug-miner-xtalpi-strikes-gold-400m-infusion-its-second-vc-megaround-a-year (Accessed on: Mar. 02, 2023).
[103]
XtalPi. Available from: https://www.jingtaikeji.com/en/(Accessed on: Mar. 02, 2023).
[104]
Sun, G.; Jin, Y.; Li, S.; Yang, Z.; Shi, B.; Chang, C.; Abramov, Y.A. Virtual coformer screening by crystal structure predictions: Crucial role of crystallinity in pharmaceutical cocrystallization. J. Phys. Chem. Lett., 2020, 11(20), 8832-8838.
[http://dx.doi.org/10.1021/acs.jpclett.0c02371] [PMID: 32969658]
[105]
Numerate to Use AI Platform in Developing Drugs for Takeda. Available from: https://www.genengnews.com/topics/translational-medicine/numerate-to-use-ai-platform-in-developing-drugs-for-takeda/(Accessed on: Mar. 02, 2023).
[106]
Numerate - Products, Competitors, Financials, Employees, Headquarters Location. Available from: https://www.cbinsights.com/company/numerate(Accessed on: Mar. 02, 2023).
[107]
Numerate and Lundbeck Partner to Apply AI Drug Discovery to Unlock Challenges of Neuroscience Research. Available from: https://www.businesswire.com/news/home/20190107005135/en/Numerate-and-Lundbeck-Partner-to-Apply-AI-Drug-Discovery-to-Unlock-Challenges-of-Neuroscience-Research (Accessed on: Mar. 02, 2023).
[108]
Inside perspectives: Numerate aims for speedier drug development with artificial intelligence - WuXi XPress: for WuXi news and R&D insights. Available from: https://wxpress.wuxiapptec.com/inside-perspectives-numerate-aims-speedier-drug-development-artificial-intelligence/ (Accessed on: Mar. 02, 2023).
[109]
Pu, L.; Naderi, M.; Liu, T.; Wu, H.C.; Mukhopadhyay, S.; Brylinski, M. eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol. Toxicol., 2019, 20(1), 2.
[http://dx.doi.org/10.1186/s40360-018-0282-6] [PMID: 30621790]
[110]
Hung, C.L.; Chen, C.C. Computational approaches for drug discovery. Drug Dev. Res., 2014, 75(6), 412-418.
[http://dx.doi.org/10.1002/ddr.21222] [PMID: 25195585]
[111]
Yang, S.Y. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov. Today, 2010, 15(11-12), 444-450.
[http://dx.doi.org/10.1016/j.drudis.2010.03.013] [PMID: 20362693]
[112]
Billionaire-backed startup puts Big Data, AI at the heart of drug discovery. Available from: https://www.fiercebiotech.com/r-d/billionaire-backed-startup-puts-big-data-ai-at-heart-of-drug-discovery(Accessed on: Mar. 02, 2023).
[113]
Welcome to recursion: The future of TechBio. Available from: https://www.recursion.com/(Accessed on: Mar. 02, 2023).
[114]
Bombicz, P. Artificial intelligence and machine learning in crystallography editorial for crystallography reviews, Issue 2 of Volume27, 2021. Crystallogr. Rev., 2021, 27(7), 51-53.
[http://dx.doi.org/10.1080/0889311X.2021.2000094]
[115]
Reddy, A.S.; Zhang, S. Polypharmacology: Drug discovery for the future. Expert Rev. Clin. Pharmacol., 2013, 6(1), 41-47.
[http://dx.doi.org/10.1586/ecp.12.74] [PMID: 23272792]
[116]
Madabhushi, A.; Lee, G. Image analysis and machine learning in digital pathology: Challenges and opportunities. Med. Image Anal., 2016, 33, 170-175.
[http://dx.doi.org/10.1016/j.media.2016.06.037] [PMID: 27423409]
[117]
Leiserson, M.D.M.; Vandin, F.; Wu, H.T.; Dobson, J.R.; Eldridge, J.V.; Thomas, J.L.; Papoutsaki, A.; Kim, Y.; Niu, B.; McLellan, M.; Lawrence, M.S.; Gonzalez-Perez, A.; Tamborero, D.; Cheng, Y.; Ryslik, G.A.; Lopez-Bigas, N.; Getz, G.; Ding, L.; Raphael, B.J. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet., 2015, 47(2), 106-114.
[http://dx.doi.org/10.1038/ng.3168] [PMID: 25501392]
[118]
Battle, A.; Brown, C.D.; Engelhardt, B.E.; Montgomery, S.B. Genetic effects on gene expression across human tissues. Nature, 2017, 550(7675), 204-213.
[http://dx.doi.org/10.1038/nature24277] [PMID: 29022597]
[119]
Kebotix. Available from: https://www.kebotix.com/(Accessed on: Mar. 02, 2023).
[120]
Al-antari, M.A.; Al-masni, M.A.; Kim, T.S. Deep learning computer-aided diagnosis for breast lesion in digital mammogram. Adv. Exp. Med. Biol., 2020, 1213, 59-72.
[http://dx.doi.org/10.1007/978-3-030-33128-3_4] [PMID: 32030663]
[121]
Brasil, S.; Pascoal, C.; Francisco, R.; Dos Reis, F.V.; Videira, P.A.; Valadão, A.G. Artificial intelligence (AI) in Rare Diseases: Is the future brighter? Genes, 2019, 10(12), 978.
[http://dx.doi.org/10.3390/genes10120978] [PMID: 31783696]
[122]
Aliper, A.; Plis, S.; Artemov, A.; Ulloa, A.; Mamoshina, P.; Zhavoronkov, A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm., 2016, 13(7), 2524-2530.
[http://dx.doi.org/10.1021/acs.molpharmaceut.6b00248] [PMID: 27200455]
[123]
Qureshi, M.; Qadir, A.; Aqil, M.; Sultana, Y.; Warsi, M.H.; Ismail, M.V.; Talegaonkar, S. Berberine loaded dermal quality by design adapted chemically engineered lipid nano-constructs-gel formulation for the treatment of skin acne. J. Drug Deliv. Sci. Technol., 2021, 66, 102805.
[http://dx.doi.org/10.1016/j.jddst.2021.102805]
[124]
Vatansever, S.; Schlessinger, A.; Wacker, D.; Kaniskan, H.Ü.; Jin, J.; Zhou, M.M.; Zhang, B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State of the arts and future directions. Med. Res. Rev., 2021, 41(3), 1427-1473.
[http://dx.doi.org/10.1002/med.21764] [PMID: 33295676]
[125]
Farghali, H.; Kutinová Canová, N.; Arora, M. The potential applications of artificial intelligence in drug discovery and development. Physiol. Res., 2021, 70(S4), S715-S722.
[http://dx.doi.org/10.33549/physiolres.934765] [PMID: 35199553]
[126]
Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol. Divers., 2021, 25(3), 1315-1360.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]
[127]
Nayarisseri, A.; Khandelwal, R.; Tanwar, P.; Madhavi, M.; Sharma, D.; Thakur, G.; Speck-Planche, A.; Singh, S.K. Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery. Curr. Drug Targets, 2021, 22(6), 631-655.
[http://dx.doi.org/10.2174/18735592MTEzsMDMnz] [PMID: 33397265]
[128]
Schirle, M.; Jenkins, J.L. Identifying compound efficacy targets in phenotypic drug discovery. Drug Discov. Today, 2016, 21(1), 82-89.
[http://dx.doi.org/10.1016/j.drudis.2015.08.001] [PMID: 26272035]
[129]
Tripathi, M.K.; Nath, A.; Singh, T.P.; Ethayathulla, A.S.; Kaur, P. Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. Mol. Divers., 2021, 25(3), 1439-1460.
[http://dx.doi.org/10.1007/s11030-021-10256-w] [PMID: 34159484]
[130]
Upreti, V.V.; Venkatakrishnan, K. Model-based meta-analysis: Optimizing research, development, and utilization of therapeutics using the totality of evidence. Clin. Pharmacol. Ther., 2019, 106(5), 981-992.
[http://dx.doi.org/10.1002/cpt.1462] [PMID: 30993679]
[131]
Lavecchia, A. Machine-learning approaches in drug discovery: Methods and applications. Drug Discov. Today, 2015, 20(3), 318-331.
[http://dx.doi.org/10.1016/j.drudis.2014.10.012] [PMID: 25448759]
[132]
Davenport, T.; Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthc. J., 2019, 6(2), 94-98.
[http://dx.doi.org/10.7861/futurehosp.6-2-94] [PMID: 31363513]
[133]
Schwalbe, N.; Wahl, B. Artificial intelligence and the future of global health. Lancet, 2020, 395(10236), 1579-1586.
[http://dx.doi.org/10.1016/S0140-6736(20)30226-9] [PMID: 32416782]
[134]
Tripathi, N.; Goshisht, M.K.; Sahu, S.K.; Arora, C. Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review. Mol. Divers., 2021, 25(3), 1643-1664.
[http://dx.doi.org/10.1007/s11030-021-10237-z] [PMID: 34110579]
[135]
Artificial intelligence in global health: A brave new world. Lancet, 2019, 393(10180), 1478.
[http://dx.doi.org/10.1016/S0140-6736(19)30814-1]

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