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Current Pharmaceutical Design

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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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Review Article

Revolutionizing Pharmaceutical Industry: The Radical Impact of Artificial Intelligence and Machine Learning

Author(s): Aashveen Chhina, Karan Trehan, Muskaan Saini, Shubham Thakur, Manjot Kaur, Navid Reza Shahtaghi, Riya Shivgotra, Bindu Soni, Anuj Modi, Hossamaldeen Bakrey and Subheet Kumar Jain*

Volume 29, Issue 21, 2023

Published on: 16 August, 2023

Page: [1645 - 1658] Pages: 14

DOI: 10.2174/1381612829666230807161421

Price: $65

Open Access Journals Promotions 2
Abstract

This article explores the significant impact of artificial intelligence (AI) and machine learning (ML) on the pharmaceutical industry, which has transformed the drug development process. AI and ML technologies provide powerful tools for analysis, decision-making, and prediction by simplifying complex procedures from drug design to formulation design. These techniques could potentially speed up the development of better medications and drug development processes, improving the lives of millions of people. However, the use of these techniques requires trained personnel and human surveillance for AI to function effectively, if not there is a possibility of errors like security breaches of personal data and bias can also occur. Thus, the present review article discusses the transformative power of AI and ML in the pharmaceutical industry and provides insights into the future of drug development and patient care.

Keywords: Artificial intelligence, machine learning, drug discovery, post-marketing surveillance, target identification, QSAR modeling, polypharmacology.

« Previous Next »
[1]
Copeland J. Artificial intelligence: A philosophical introduction. John Wiley & Sons; 1993.
[2]
Fahle S, Prinz C, Kuhlenkötter B. Systematic review on machine learning (ML) methods for manufacturing processes: Identifying artificial intelligence (AI) methods for field application. Procedia CIRP 2020; 93: 413-8.
[http://dx.doi.org/10.1016/j.procir.2020.04.109]
[3]
Kolachalama VB, Garg PS. Machine learning and medical education. NPJ Digit Med 2018; 1(1): 54.
[http://dx.doi.org/10.1038/s41746-018-0061-1] [PMID: 31304333]
[4]
Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science 2015; 349(6245): 255-60.
[http://dx.doi.org/10.1126/science.aaa8415] [PMID: 26185243]
[5]
Rathore AS, Nikita S, Thakur G, Mishra S. Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends Biotechnol 2022.
[PMID: 36117026]
[6]
Rohall SL, Auch L, Gable J, et al. An artificial intelligence approach to proactively inspire drug discovery with recommendations. J Med Chem 2020; 63(16): 8824-34.
[http://dx.doi.org/10.1021/acs.jmedchem.9b02130] [PMID: 32101427]
[7]
(a) Stewart J, Sprivulis P, Dwivedi G. Artificial intelligence and machine learning in emergency medicine. Emerg Med Australas 2018; 30(6): 870-4.
[http://dx.doi.org/10.1111/1742-6723.13145] [PMID: 30014578];
(b) Henstock PV. Artificial intelligence for pharma: Time for internal investment. Trends Pharmacol Sci 2019; 40(8): 543-6.
[http://dx.doi.org/10.1016/j.tips.2019.05.003] [PMID: 31204059]
[8]
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019; 380(14): 1347-58.
[http://dx.doi.org/10.1056/NEJMra1814259] [PMID: 30943338]
[9]
Gunčar G, Kukar M, Notar M, et al. An application of machine learning to haematological diagnosis. Sci Rep 2018; 8(1): 411.
[http://dx.doi.org/10.1038/s41598-017-18564-8] [PMID: 29323142]
[10]
Shafiq M, Yu X, Laghari AA, Yao L, Karn NK, Abdessamia F. Network traffic classification techniques and comparative analysis using machine learning algorithms. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC) 2016, pp. 2451-5.
[http://dx.doi.org/10.1109/CompComm.2016.7925139]
[11]
Dallora AL, Eivazzadeh S, Mendes E, Berglund J, Anderberg P. Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review. PLoS One 2017; 12(6): e0179804.
[http://dx.doi.org/10.1371/journal.pone.0179804] [PMID: 28662070]
[12]
Koohy H. The rise and fall of machine learning methods in biomedical research. F1000 Res 2017; 6: 2012.
[http://dx.doi.org/10.12688/f1000research.13016.1] [PMID: 29375816]
[13]
Le TL. Fuzzy C-means clustering interval type-2 cerebellar model articulation neural network for medical data classification. IEEE Access 2019; 7: 20967-73.
[http://dx.doi.org/10.1109/ACCESS.2019.2895636]
[14]
Schmauch B, Herent P, Jehanno P, et al. Diagnosis of focal liver lesions from ultrasound using deep learning. Diagn Interv Imaging 2019; 100(4): 227-33.
[http://dx.doi.org/10.1016/j.diii.2019.02.009] [PMID: 30926443]
[15]
Bakator M, Radosav D. Deep learning and medical diagnosis: A review of literature. Multimodal Technol Interact 2018; 2(3): 47.
[http://dx.doi.org/10.3390/mti2030047]
[16]
Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: General overview. Korean J Radiol 2017; 18(4): 570-84.
[http://dx.doi.org/10.3348/kjr.2017.18.4.570] [PMID: 28670152]
[17]
(a) Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol 2017; 10(3): 257-73.
[http://dx.doi.org/10.1007/s12194-017-0406-5] [PMID: 28689314];
(b) Mwandau B, Nyanchama M. Investigating keystroke dynamics as a two-factor biometric security. Doctoral dissertation, Strathmore University.
[18]
Ginsburg GS, Phillips KA. Precision medicine: From science to value. Health Aff 2018; 37(5): 694-701.
[http://dx.doi.org/10.1377/hlthaff.2017.1624] [PMID: 29733705]
[19]
Johnson KB, Wei WQ, Weeraratne D, et al. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci 2021; 14(1): 86-93.
[http://dx.doi.org/10.1111/cts.12884] [PMID: 32961010]
[20]
Hessler G, Baringhaus KH. Artificial intelligence in drug design. Molecules 2018; 23(10): 2520.
[http://dx.doi.org/10.3390/molecules23102520] [PMID: 30279331]
[21]
Liu B, Ramsundar B, Kawthekar P, et al. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent Sci 2017; 3(10): 1103-13.
[http://dx.doi.org/10.1021/acscentsci.7b00303] [PMID: 29104927]
[22]
Staszak M, Staszak K, Wieszczycka K, Bajek A, Roszkowski K, Tylkowski B. Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. Wiley Interdiscip Rev Comput Mol Sci 2022; 12(2): e1568.
[http://dx.doi.org/10.1002/wcms.1568]
[23]
(a) Moingeon P, Kuenemann M, Guedj M. Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine. Drug Discov Today 2022; 27(1): 215-22.
[http://dx.doi.org/10.1016/j.drudis.2021.09.006] [PMID: 34555509];
(b) Mak KK, Pichika MR. Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today 2019; 24(3): 773-80.
[http://dx.doi.org/10.1016/j.drudis.2018.11.014] [PMID: 30472429]
[24]
Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular denovo design through deep reinforcement learning. J Cheminform 2017; 9(1): 48.
[http://dx.doi.org/10.1186/s13321-017-0235-x] [PMID: 29086083]
[25]
Rodrigues T, Werner M, Roth J, et al. Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor. Chem Sci 2018; 9(34): 6899-903.
[http://dx.doi.org/10.1039/C8SC02634C] [PMID: 30310622]
[26]
Lee EJ, Kim YH, Kim N, Kang DW. Deep into the brain: Artificial intelligence in stroke imaging. J Stroke 2017; 19(3): 277-85.
[http://dx.doi.org/10.5853/jos.2017.02054] [PMID: 29037014]
[27]
Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. 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]
[28]
Álvarez-Machancoses Ó, Fernández-Martínez JL. Using artificial intelligence methods to speed up drug discovery. Expert Opin Drug Discov 2019; 14(8): 769-77.
[http://dx.doi.org/10.1080/17460441.2019.1621284] [PMID: 31140873]
[29]
Dana D, Gadhiya SV, St Surin LG, et al. Deep learning in drug discovery and medicine; scratching the surface. Molecules 2018; 23(9): 2384.
[http://dx.doi.org/10.3390/molecules23092384] [PMID: 30231499]
[30]
Cavasotto CN, Di Filippo JI. Artificial intelligence in the early stages of drug discovery. Arch Biochem Biophys 2021; 698: 108730.
[http://dx.doi.org/10.1016/j.abb.2020.108730] [PMID: 33347838]
[31]
Jiménez-Luna J, Grisoni F, Schneider G. Drug discovery with explainable artificial intelligence. Nat Mach Intell 2020; 2(10): 573-84.
[http://dx.doi.org/10.1038/s42256-020-00236-4]
[32]
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25(3): 1315-60.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]
[33]
Proschak E, Stark H, Merk D. Polypharmacology by design: A medicinal chemist’s perspective on multitargeting compounds. J Med Chem 2019; 62(2): 420-44.
[http://dx.doi.org/10.1021/acs.jmedchem.8b00760] [PMID: 30035545]
[34]
Chaudhari R, Fong LW, Tan Z, Huang B, Zhang S. An up-to-date overview of computational polypharmacology in modern drug discovery. Expert Opin Drug Discov 2020; 15(9): 1025-44.
[http://dx.doi.org/10.1080/17460441.2020.1767063] [PMID: 32452701]
[35]
Awale M, Reymond JL. The polypharmacology browser: A web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data. J Cheminform 2017; 9(1): 11.
[http://dx.doi.org/10.1186/s13321-017-0199-x] [PMID: 28270862]
[36]
Das S, Dey R, Nayak AK. Artificial intelligence in pharmacy. Indian J Pharm Educ 2021; 55(2): 304-18.
[http://dx.doi.org/10.5530/ijper.55.2.68]
[37]
Da C, Zhang D, Stashko M, et al. Data-driven construction of antitumor agents with controlled polypharmacology. J Am Chem Soc 2019; 141(39): 15700-9.
[http://dx.doi.org/10.1021/jacs.9b08660] [PMID: 31497954]
[38]
Moya-García AA, Ranea JAG. Insights into polypharmacology from drug-domain associations. Bioinformatics 2013; 29(16): 1934-7.
[http://dx.doi.org/10.1093/bioinformatics/btt321] [PMID: 23740740]
[39]
Singh AV, Ansari MHD, Rosenkranz D, et al. Artificial intelligence and machine learning in computational nanotoxicology: Unlocking and empowering nanomedicine. Adv Healthc Mater 2020; 9(17): 1901862.
[http://dx.doi.org/10.1002/adhm.201901862] [PMID: 32627972]
[40]
Wang T, Yuan X, Wu MB, Lin JP, Yang LR. The advancement of multidimensional QSAR for novel drug discovery: Where are we headed? Expert Opin Drug Discov 2017; 12(8): 1-16.
[http://dx.doi.org/10.1080/17460441.2017.1336157] [PMID: 28562095]
[41]
Consonni V, Todeschini R. Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing/Volume II: Appendices, References. John Wiley & Sons 2009.
[42]
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): 949-59.
[http://dx.doi.org/10.1080/17460441.2021.1909567] [PMID: 33779453]
[43]
Fujita T, Winkler DA. Understanding the roles of the “two QSARs”. J Chem Inf Model 2016; 56(2): 269-74.
[http://dx.doi.org/10.1021/acs.jcim.5b00229] [PMID: 26754147]
[44]
Vatansever S, Schlessinger A, Wacker D, et al. 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): 1427-73.
[http://dx.doi.org/10.1002/med.21764] [PMID: 33295676]
[45]
Martin EJ, Polyakov VR, Tian L, Perez RC. Profile-QSAR 2.0: kinase virtual screening accuracy comparable to four-concentration IC50s for realistically novel compounds. J Chem Inf Model 2017; 57(8): 2077-88.
[http://dx.doi.org/10.1021/acs.jcim.7b00166] [PMID: 28651433]
[46]
Simeon S, Jongkon N. Construction of quantitative structure activity relationship (QSAR) Models to predict potency of structurally diversed janus kinase 2 inhibitors. Molecules 2019; 24(23): 4393.
[http://dx.doi.org/10.3390/molecules24234393] [PMID: 31805692]
[47]
Shamsara J. A random forest model to predict the activity of a large set of soluble epoxide hydrolase inhibitors solely based on a set of simple fragmental descriptors. Comb Chem High Throughput Screen 2019; 22(8): 555-69.
[http://dx.doi.org/10.2174/1386207322666191016110232] [PMID: 31622216]
[48]
Marchese Robinson RL, Palczewska A, Palczewski J, Kidley N. Comparison of the predictive performance and interpretability of random forest and linear models on benchmark data sets. J Chem Inf Model 2017; 57(8): 1773-92.
[http://dx.doi.org/10.1021/acs.jcim.6b00753] [PMID: 28715209]
[49]
Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V. Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072. 2015.
[50]
Winkler DA. Role of artificial intelligence and machine learning in nanosafety. Small 2020; 16(36): 2001883.
[http://dx.doi.org/10.1002/smll.202001883] [PMID: 32537842]
[51]
Epa VC, Burden FR, Tassa C, Weissleder R, Shaw S, Winkler DA. Modeling biological activities of nanoparticles. Nano Lett 2012; 12(11): 5808-12.
[http://dx.doi.org/10.1021/nl303144k] [PMID: 23039907]
[52]
Wang Q, Feng Y, Huang J, Wang T, Cheng G. A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine. PLoS One 2017; 12(4): e0176486.
[http://dx.doi.org/10.1371/journal.pone.0176486] [PMID: 28453576]
[53]
Ferrero E, Dunham I, Sanseau P. In silico prediction of novel therapeutic targets using gene-disease association data. J Transl Med 2017; 15(1): 182.
[http://dx.doi.org/10.1186/s12967-017-1285-6] [PMID: 28851378]
[54]
Chan HCS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing drug discovery via artificial intelligence. Trends Pharmacol Sci 2019; 40(8): 592-604.
[http://dx.doi.org/10.1016/j.tips.2019.06.004] [PMID: 31320117]
[55]
Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019; 18(6): 463-77.
[http://dx.doi.org/10.1038/s41573-019-0024-5] [PMID: 30976107]
[56]
Menden MP, Iorio F, Garnett M, et al. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS One 2013; 8(4): e61318.
[http://dx.doi.org/10.1371/journal.pone.0061318] [PMID: 23646105]
[57]
Awale M, Reymond JL. Polypharmacology browser PPB2: Target prediction combining nearest neighbors with machine learning. J Chem Inf Model 2019; 59(1): 10-7.
[http://dx.doi.org/10.1021/acs.jcim.8b00524] [PMID: 30558418]
[58]
Agamah FE, Mazandu GK, Hassan R, et al. Computational/in silico methods in drug target and lead prediction. Brief Bioinform 2020; 21(5): 1663-75.
[http://dx.doi.org/10.1093/bib/bbz103] [PMID: 31711157]
[59]
You Y, Lai X, Pan Y, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7(1): 156.
[http://dx.doi.org/10.1038/s41392-022-00994-0] [PMID: 35538061]
[60]
Jeon J, Nim S, Teyra J, et al. A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening. Genome Med 2014; 6(7): 57.
[http://dx.doi.org/10.1186/s13073-014-0057-7] [PMID: 25165489]
[61]
McMillan EA, Ryu MJ, Diep CH, et al. Chemistry-first approach for nomination of personalized treatment in lung cancer. Cell 2018; 173(4): 864-878.e29.
[http://dx.doi.org/10.1016/j.cell.2018.03.028] [PMID: 29681454]
[62]
Nidhi , Glick M, Davies JW, Jenkins JL. Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model 2006; 46(3): 1124-33.
[http://dx.doi.org/10.1021/ci060003g] [PMID: 16711732]
[63]
Lysenko A, Sharma A, Boroevich KA, Tsunoda T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci Alliance 2018; 1(6): e201800098.
[http://dx.doi.org/10.26508/lsa.201800098] [PMID: 30515477]
[64]
Wang Z, Liang L, Yin Z, Lin J. Improving chemical similarity ensemble approach in target prediction. J Cheminform 2016; 8(1): 20.
[http://dx.doi.org/10.1186/s13321-016-0130-x] [PMID: 27110288]
[65]
Attene-Ramos MS, Miller N, Huang R, et al. The Tox21 robotic platform for the assessment of environmental chemicals: From vision to reality. Drug Discov Today 2013; 18(15-16): 716-23.
[http://dx.doi.org/10.1016/j.drudis.2013.05.015] [PMID: 23732176]
[66]
Unterthiner T, Mayr A, Klambauer G, Hochreiter S. Toxicity prediction using deep learning. arXiv preprint arXiv 2015.
[67]
Gayvert KM, Madhukar NS, Elemento O. A data-driven approach to predicting successes and failures of clinical trials. Cell Chem Biol 2016; 23(10): 1294-301.
[http://dx.doi.org/10.1016/j.chembiol.2016.07.023] [PMID: 27642066]
[68]
Goh GB, Hodas NO, Siegel C, Vishnu A. Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties. 2017.
[69]
Preuer K, Lewis RPI, Hochreiter S, Bender A, Bulusu KC, Klambauer G. DeepSynergy: Predicting anti-cancer drug synergy with deep learning. Bioinformatics 2018; 34(9): 1538-46.
[http://dx.doi.org/10.1093/bioinformatics/btx806] [PMID: 29253077]
[70]
Luechtefeld T, Marsh D, Rowlands C, Hartung T. Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol Sci 2018; 165(1): 198-212.
[http://dx.doi.org/10.1093/toxsci/kfy152] [PMID: 30007363]
[71]
Srivastava A, Siddiqui S, Ahmad R, Mehrotra S, Ahmad B, Srivastava AN. Exploring nature’s bounty: identification of Withania somnifera as a promising source of therapeutic agents against COVID-19 by virtual screening and in silico evaluation. J Biomol Struct Dyn 2022; 40(4): 1858-908.
[http://dx.doi.org/10.1080/07391102.2020.1835725] [PMID: 33246398]
[72]
Pires DEV, Blundell TL, Ascher DB. pkCSM: predicting smallmolecule pharmacokinetic and toxicity properties using graphbased signatures. J Med Chem 2015; 58(9): 4066-72.
[http://dx.doi.org/10.1021/acs.jmedchem.5b00104] [PMID: 25860834]
[73]
Cheng F, Li W, Zhou Y, et al. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. 2012; 52(11): 3099-105.
[74]
Sander T, Freyss J, von Korff M, Rufener C. DataWarrior: An open-source program for chemistry aware data visualization and analysis. J Chem Inf Model 2015; 55(2): 460-73.
[http://dx.doi.org/10.1021/ci500588j] [PMID: 25558886]
[75]
Rudik AV, Bezhentsev VM, Dmitriev AV, et al. MetaTox: Web application for predicting structure and toxicity of xenobiotics’ metabolites. J Chem Inf Model 2017; 57(4): 638-42.
[http://dx.doi.org/10.1021/acs.jcim.6b00662] [PMID: 28345905]
[76]
Trunzer M, Faller B, Zimmerlin A. Metabolic soft spot identification and compound optimization in early discovery phases using MetaSite and LC-MS/MS validation. J Med Chem 2009; 52(2): 329-35.
[http://dx.doi.org/10.1021/jm8008663] [PMID: 19108654]
[77]
Laoui A, Polyakov VR. Web services as applications’ integration tool: QikProp case study. J Comput Chem 2011; 32(9): 1944-51.
[http://dx.doi.org/10.1002/jcc.21778] [PMID: 21455963]
[78]
Dong J, Wang NN, Yao ZJ, et al. ADMETlab: A platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminform 2018; 10(1): 29.
[http://dx.doi.org/10.1186/s13321-018-0283-x] [PMID: 29943074]
[79]
Zhang L, Ai H, Chen W, et al. CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods. Sci Rep 2017; 7(1): 2118.
[http://dx.doi.org/10.1038/s41598-017-02365-0] [PMID: 28522849]
[80]
Lagorce D, Bouslama L, Becot J, Miteva MA, Villoutreix BO. FAF-Drugs4: Free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinformatics 2017; 33(22): 3658-60.
[http://dx.doi.org/10.1093/bioinformatics/btx491] [PMID: 28961788]
[81]
Podlewska S, Kafel R. MetStabOn-online platform for metabolic stability predictions. Int J Mol Sci 2018; 19(4): 1040.
[http://dx.doi.org/10.3390/ijms19041040] [PMID: 29601530]
[82]
Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017; 7(1): 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[83]
Schyman P, Liu R, Desai V, Wallqvist A. vNN web server for ADMET predictions. Front Pharmacol 2017; 8: 889.
[http://dx.doi.org/10.3389/fphar.2017.00889] [PMID: 29255418]
[84]
Schneider P, Walters WP, Plowright AT, et al. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2020; 19(5): 353-64.
[http://dx.doi.org/10.1038/s41573-019-0050-3] [PMID: 31801986]
[85]
Liu B, He H, Luo H, Zhang T, Jiang J. Artificial intelligence and big data facilitated targeted drug discovery. Stroke Vasc Neurol 2019; 4(4): 206-13.
[http://dx.doi.org/10.1136/svn-2019-000290] [PMID: 32030204]
[86]
Yang H, Lou C, Sun L, et al. admetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 2019; 35(6): 1067-9.
[http://dx.doi.org/10.1093/bioinformatics/bty707] [PMID: 30165565]
[87]
Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 2018; 46(W1): W257-63.
[http://dx.doi.org/10.1093/nar/gky318] [PMID: 29718510]
[88]
Wang YW, Huang L, Jiang SW, Li K, Zou J, Yang SY. CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens. Food Chem Toxicol 2020; 135: 110921.
[http://dx.doi.org/10.1016/j.fct.2019.110921] [PMID: 31669597]
[89]
Patel RD, Prasanth Kumar S, Pandya HA, Solanki HA. MDCKpred: A web-tool to calculate MDCK permeability coefficient of small molecule using membrane-interaction chemical features. Toxicol Mech Methods 2018; 28(9): 685-98.
[http://dx.doi.org/10.1080/15376516.2018.1499840] [PMID: 29998769]
[90]
Venkatraman V. FP-ADMET: A compendium of fingerprint-based ADMET prediction models. J Cheminform 2021; 13(1): 75.
[http://dx.doi.org/10.1186/s13321-021-00557-5] [PMID: 34583740]
[91]
Cáceres EL, Tudor M, Cheng AC. Deep learning approaches in predicting ADMET properties. Future Med Chem 2020; 12(22): 1995-9.
[http://dx.doi.org/10.4155/fmc-2020-0259] [PMID: 33124448]
[92]
Kramer C, Ting A, Zheng H, et al. Learning medicinal chemistry absorption, distribution, metabolism, excretion, and toxicity (ADMET) rules from cross-company matched molecular pairs analysis (MMPA) miniperspective. J Med Chem 2018; 61(8): 3277-92.
[http://dx.doi.org/10.1021/acs.jmedchem.7b00935] [PMID: 28956609]
[93]
Yang M, Chen J, Xu L, et al. A novel adaptive ensemble classification framework for ADME prediction. RSC Advances 2018; 8(21): 11661-83.
[http://dx.doi.org/10.1039/C8RA01206G] [PMID: 35542768]
[94]
Bocci G, Carosati E, Vayer P, Arrault A, Lozano S, Cruciani G. ADME-Space: A new tool for medicinal chemists to explore ADME properties. Sci Rep 2017; 7(1): 6359.
[http://dx.doi.org/10.1038/s41598-017-06692-0] [PMID: 28743970]
[95]
(a) Joudaki D, Shafiei F. QSPR models to predict thermodynamic properties of cycloalkanes using molecular descriptors and GAMLR method. Curr Computeraided Drug Des 2020; 16(1): 6-16.
[http://dx.doi.org/10.2174/1573409915666190227230744] [PMID: 30827257];
(b) Li S, Wu S, Wang L, Li F, Jiang H, Bai F. Recent advances in predicting protein-protein interactions with the aid of artificial intelligence algorithms. Curr Opin Struct Biol 2022; 73: 102344.
[http://dx.doi.org/10.1016/j.sbi.2022.102344] [PMID: 35219216]
[96]
Lu H, Lu L, Skolnick J. Development of unified statistical potentials describing protein-protein interactions. Biophys J 2003; 84(3): 1895-901.
[http://dx.doi.org/10.1016/S0006-3495(03)74997-2] [PMID: 12609891]
[97]
Singh R, Park D, Xu J, Hosur R, Berger B. Struct2Net: A web service to predict protein-protein interactions using a structure-based approach. Nucleic Acids Res 2010; 38(Web Server) (Suppl. 2): W508-15.
[http://dx.doi.org/10.1093/nar/gkq481] [PMID: 20513650]
[98]
Rao VS, Srinivas K, Sujini GN, Kumar GN. Protein-protein interaction detection: Methods and analysis. Int J Proteomics 2014; 147648.
[http://dx.doi.org/10.1155/2014/147648]
[99]
Deng L, Guan J, Wei X, Yi Y, Zhang QC, Zhou S. Boosting prediction performance of protein-protein interaction hot spots by using structural neighborhood properties. J Comput Biol 2013; 20(11): 878-91.
[http://dx.doi.org/10.1089/cmb.2013.0083] [PMID: 24134392]
[100]
Torchet R, Druart K, Ruano LC, et al. The iPPI-DB initiative: A community-centered database of protein–protein interaction modulators. Bioinformatics 2021; 37(1): 89-96.
[http://dx.doi.org/10.1093/bioinformatics/btaa1091] [PMID: 33416858]
[101]
Hamon V, Bourgeas R, Ducrot P, et al. 2P2I HUNTER : A tool for filtering orthosteric protein–protein interaction modulators via a dedicated support vector machine. J R Soc Interface 2014; 11(90): 20130860.
[http://dx.doi.org/10.1098/rsif.2013.0860] [PMID: 24196694]
[102]
Gupta P, Mohanty D. SMMPPI: A machine learning-based approach for prediction of modulators of protein–protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2. Brief Bioinform 2021; 22(5): bbab111.
[http://dx.doi.org/10.1093/bib/bbab111] [PMID: 33839740]
[103]
Dai X, Xu F, Wang S, Mundra PA, Zheng J. PIKE-R2P: Protein–protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction. BMC Bioinformatics 2021; 22(S6) (Suppl. 6): 139.
[http://dx.doi.org/10.1186/s12859-021-04022-w] [PMID: 34078261]
[104]
Czibula G, Albu AI, Bocicor MI, Chira C. AutoPPI: An ensemble of deep autoencoders for protein–protein interaction prediction. Entropy 2021; 23(6): 643.
[http://dx.doi.org/10.3390/e23060643] [PMID: 34064042]
[105]
Chen W, Wang S, Song T, Li X, Han P, Gao C. DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction. BMC Genomics 2022; 23(1): 555.
[http://dx.doi.org/10.1186/s12864-022-08772-6] [PMID: 35922751]
[106]
Wee J, Xia K. Persistent spectral based ensemble learning (Per-Spect-EL) for protein–protein binding affinity prediction. Brief Bioinform 2022; 23(2): bbac024.
[http://dx.doi.org/10.1093/bib/bbac024] [PMID: 35189639]
[107]
Zhang L. CASTELO-a combined machine learning and molecular modeling for drug discovery and protein-protein interaction optimization. InAmerican Chemical Society (ACS) Fall Meeting 2022; 22(1): 338.
[108]
Tian K, Shao M, Wang Y, Guan J, Zhou S. Boosting compound-protein interaction prediction by deep learning. Methods 2016; 110: 64-72.
[http://dx.doi.org/10.1016/j.ymeth.2016.06.024] [PMID: 27378654]
[109]
Ashley EA. Towards precision medicine. Nat Rev Genet 2016; 17(9): 507-22.
[http://dx.doi.org/10.1038/nrg.2016.86] [PMID: 27528417]
[110]
Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM. Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities. Inf Fusion 2019; 50: 71-91.
[http://dx.doi.org/10.1016/j.inffus.2018.09.012] [PMID: 30467459]
[111]
Hoadley KA, Yau C, Wolf DM, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 2014; 158(4): 929-44.
[http://dx.doi.org/10.1016/j.cell.2014.06.049] [PMID: 25109877]
[112]
Ting DSW, Liu Y, Burlina P, Xu X, Bressler NM, Wong TY. AI for medical imaging goes deep. Nat Med 2018; 24(5): 539-40.
[http://dx.doi.org/10.1038/s41591-018-0029-3] [PMID: 29736024]
[113]
Gore JC. Artificial intelligence in medical imaging. MRI 2020; 68: A1-4.
[114]
Kolluri S, Lin J, Liu R, Zhang Y, Zhang W. Machine learning and artificial intelligence in pharmaceutical research and development: A review. AAPS J 2022; 24(1): 19.
[http://dx.doi.org/10.1208/s12248-021-00644-3] [PMID: 34984579]
[115]
Kumar V, L M. Predictive analytics: A review of trends and techniques. Int J Comput Appl 2018; 182(1): 31-7.
[http://dx.doi.org/10.5120/ijca2018917434]
[116]
Lamberti MJ, Wilkinson M, Donzanti BA, et al. A study on the application and use of artificial intelligence to support drug development. Clin Ther 2019; 41(8): 1414-26.
[http://dx.doi.org/10.1016/j.clinthera.2019.05.018] [PMID: 31248680]
[117]
Bhatt A. Artificial intelligence in managing clinical trial design and conduct: Man and machine still on the learning curve? Perspect Clin Res 2021; 12(1): 1-3.
[http://dx.doi.org/10.4103/picr.PICR_312_20] [PMID: 33816201]
[118]
Weissler EH, Naumann T, Andersson T, et al. The role of machine learning in clinical research: transforming the future of evidence generation. Trials 2021; 22(1): 1-5.
[PMID: 33397449]
[119]
Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci 2019; 40(8): 577-91.
[http://dx.doi.org/10.1016/j.tips.2019.05.005] [PMID: 31326235]
[120]
Rabaan AA, Bakhrebah MA, AlSaihati H, et al. Artificial intelligence for clinical diagnosis and treatment of prostate cancer. Cancers 2022; 14(22): 5595.
[http://dx.doi.org/10.3390/cancers14225595] [PMID: 36428686]
[121]
Kim CH, Bhattacharjee S, Prakash D, et al. Artificial intelligence techniques for prostate cancer detection through dual-channel tissue feature engineering. Cancers 2021; 13(7): 1524.
[http://dx.doi.org/10.3390/cancers13071524] [PMID: 33810251]
[122]
Spangler S, Wilkins AD, Bachman BJ, et al. Automated hypothesis generation based on mining scientific literature. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014, pp. 1877-86.
[http://dx.doi.org/10.1145/2623330.2623667]
[123]
Cruz Rivera S, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Lancet Digit Health 2020; 2(10): e549-60.
[http://dx.doi.org/10.1016/S2589-7500(20)30219-3] [PMID: 33328049]
[124]
Dasgupta N, Schnoll SH. Signal detection in post-marketing surveillance for controlled substances. Drug Alcohol Depend 2009; 105 (Suppl. 1): S33-41.
[http://dx.doi.org/10.1016/j.drugalcdep.2009.05.019] [PMID: 19616902]
[125]
Alomar M, Tawfiq AM, Hassan N, Palaian S. Post marketing surveillance of suspected adverse drug reactions through spontaneous reporting: Current status, challenges and the future. Ther Adv Drug Saf 2020; 11: 2042098620938595.
[http://dx.doi.org/10.1177/2042098620938595] [PMID: 32843958]
[126]
Caster O, Sandberg L, Bergvall T, Watson S, Norén GN. vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use. Pharmacoepidemiol Drug Saf 2017; 26(8): 1006-10.
[http://dx.doi.org/10.1002/pds.4247] [PMID: 28653790]
[127]
Kumar A. Past, present and future of pharmacovigilance in India. Syst Rev Pharm 2011; 2(1): 55.
[http://dx.doi.org/10.4103/0975-8453.83440]
[128]
Wani P, Shelke A, Marwadi M, et al. Role of artificial intelligence in pharmacovigilance: A concise review. J Pharm Negat Results 2022; 6149.
[129]
Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med 2016; 375(13): 1216-9.
[http://dx.doi.org/10.1056/NEJMp1606181] [PMID: 27682033]
[130]
Patel J, Patel D, Meshram D. Artificial intelligence in pharma industry-A rising concept. J Adv Pharmacogn 2021; 1(2).
[131]
Makne PD, Sontakke SS, Lakade RD, Tompe AS, Patil SS. Artificial intelligence: A review. 2022.
[132]
Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: Challenges and opportunities for the pharmaceutical industries. Mol Divers 2021; 26(3): 1-21.
[PMID: 34686947]
[133]
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.
[http://dx.doi.org/10.3390/ijerph18010271] [PMID: 33401373]
[134]
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng 2018; 2(10): 719-31.
[http://dx.doi.org/10.1038/s41551-018-0305-z] [PMID: 31015651]
[135]
Kirby JC, Speltz P, Rasmussen LV, et al. PheKB: A catalog and workflow for creating electronic phenotype algorithms for transportability. J Am Med Inform Assoc 2016; 23(6): 1046-52.
[http://dx.doi.org/10.1093/jamia/ocv202] [PMID: 27026615]
[136]
Vijayan RSK, Kihlberg J, Cross JB, Poongavanam V. Enhancing preclinical drug discovery with artificial intelligence. Drug Discov Today 2022; 27(4): 967-84.
[http://dx.doi.org/10.1016/j.drudis.2021.11.023] [PMID: 34838731]
[137]
Amarasingham R, Patzer RE, Huesch M, Nguyen NQ, Xie B. Implementing electronic health care predictive analytics: considerations and challenges. Health Aff 2014; 33(7): 1148-54.
[http://dx.doi.org/10.1377/hlthaff.2014.0352] [PMID: 25006140]
[138]
Sniderman AD, D’Agostino RB Sr, Pencina MJ. The role of physicians in the era of predictive analytics. JAMA 2015; 314(1): 25-6.
[http://dx.doi.org/10.1001/jama.2015.6177] [PMID: 26151261]
[139]
Krumholz HM. Big data and new knowledge in medicine: The thinking, training, and tools needed for a learning health system. Health Aff 2014; 33(7): 1163-70.
[http://dx.doi.org/10.1377/hlthaff.2014.0053] [PMID: 25006142]
[140]
(a) Lyell D, Coiera E. Automation bias and verification complexity: A systematic review. J Am Med Inform Assoc 2017; 24: 423-31.;
(b) Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA 2017; 318: 517-8.
[141]
Castelvecchi D. Can we open the black box of AI? Nature 2016; 538(7623): 20-3.
[http://dx.doi.org/10.1038/538020a] [PMID: 27708329]
[142]
Jiang H, Kim B, Guan M, Gupta M. To trust or not to trust a classifier.Advances in neural information processing systemsNew York: Curran Associates 2018; 31: pp. 5541-52.
[143]
Cohen IG, Amarasingham R, Shah A, Xie B, Lo B. The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff 2014; 33(7): 1139-47.
[http://dx.doi.org/10.1377/hlthaff.2014.0048] [PMID: 25006139]
[144]
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med 2019; 25(1): 24-9.
[http://dx.doi.org/10.1038/s41591-018-0316-z] [PMID: 30617335]
[145]
Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med 2019; 25(1): 44-56.
[http://dx.doi.org/10.1038/s41591-018-0300-7] [PMID: 30617339]
[146]
Shah R, Patel T, Freedman JE. Circulating extracellular vesicles in human disease. N Engl J Med 2018; 379(10): 958-66.
[http://dx.doi.org/10.1056/NEJMra1704286] [PMID: 30184457]

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