Use AI tools to create new drugs

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2024 | Volume :02 | Issue : 02 | Page : –
By
vector

R. Ganesh.,

  1. Ex-Assistant Marketing Officer, Marketing Department, Savitribai Phule Pune University, Pune, Maharashtra, India

Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_129472’);});Edit Abstract & Keyword

The emergence of artificial intelligence (AI) in pharmaceutical research [in drug discovery] is a revolution in pharmaceutical research, often combining computational methods with traditional research methods to solve problems.This review article describes various applications of AI at various stages of drug development and highlights significant advances and approaches.He explores the critical role of intelligence in drug design, polypharmacology, drug synthesis, drug repurposing, and prediction of drug properties such as toxicity, biological activity, and physicochemical properties. Although the progress in AI is encouraging, this article also addresses the challenges and limitations facing the field, including data quality, performance, computational capability of theory, and ethical reasoning. Providing a broad overview of the role of AI in drug discovery, this article highlights the potential of this technology to improve drug development while also acknowledging the challenges that must be overcome to achieve its results.

Keywords: three-dimensional (3D), drug-protein interactions (DPIs), artificial intelligence (AI), variational autoencoders (VAEs)

[This article belongs to International Journal of Bioinformatics and Computational Biology (ijbcb)]

How to cite this article:
R. Ganesh.. Use AI tools to create new drugs. International Journal of Bioinformatics and Computational Biology. 2024; 02(02):-.
How to cite this URL:
R. Ganesh.. Use AI tools to create new drugs. International Journal of Bioinformatics and Computational Biology. 2024; 02(02):-. Available from: https://journals.stmjournals.com/ijbcb/article=2024/view=0

References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_129472’);});Edit

Abbas MK, Rassam A, Karamshahi F, Abunora R, Abouseada M. The Role of AI in Drug Discovery. Chembiochem. 2024 Jul 15;25(14):e202300816.
2. Wagner J, Dahlem AM, Hudson LD, Terry SF, Altman RB, Gilliland CT, DeFeo C, Austin CP. A dynamic map for learning, communicating, navigating and improving therapeutic development. Nat Rev Drug Discov. 2018 Feb;17(2):150
3. Tambuyzer E, Vandendriessche B, Austin CP, Brooks PJ, Larsson K, Miller Needleman KI, Valentine J, Davies K, Groft SC, Preti R, Oprea TI, Prunotto M. Therapies for rare diseases: therapeutic modalities, progress and challenges ahead. Nat Rev Drug Discov. 2020 Feb;19(2):93-111
4. Khan SR, Baghdasarian A, Fahlman RP, Michail K, Siraki AG. Current status and future prospects of toxicogenomics in drug discovery. Drug Discov Today. 2014 May;19(5):562-78.
5. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ. 2016 May;47:20-33.
6. Khan SR, Al Rijjal D, Piro A, Wheeler MB. Integration of AI and traditional medicine in drug discovery. Drug Discov Today. 2021 Apr;26(4):982-992.
7. Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. Mol Ther Nucleic Acids. 2023 Feb 18;31:691-702
8. Bordukova M, Makarov N, Rodriguez-Esteban R, Schmich F, Menden MP. Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. Expert Opin Drug Discov. 2024 Jan-Jun;19(1):33-42.
9. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr;69S:S36-S40..
10. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021 Jan;26(1):80-93.
11. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2018). Artificial Intelligence and the Public Sector—Applications and Challenges. International Journal of Public Administration, 42(7), 596–615.
12. Ali, K.A., Mohin, S., Mondal, P. et al. Influence of artificial intelligence in modern pharmaceutical formulation and drug development. Futur J Pharm Sci 10, 53 2024
13. Lamberti MJ, Wilkinson M, Donzanti BA, Wohlhieter GE, Parikh S, Wilkins RG, Getz K. A Study on the Application and Use of Artificial Intelligence to Support Drug Development. Clin Ther. 2019 Aug;41(8):1414-1426..
14. Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics. 2023 Jul 10;15(7):1916.
15. 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 May;26(5):1256-1264
16. Deng J, Yang Z, Ojima I, Samaras D, Wang F. Artificial intelligence in drug discovery: applications and techniques. Brief Bioinform. 2022 Jan 17;23(1)
17. Segall M. Advances in multiparameter optimization methods for de novo drug design. Expert Opin Drug Discov. 2014 Jul;9(7):803-17.
18.Rushikesh Dhudum, Ankit Ganeshpurkar, Pawar A. Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications. Drugs and Drug Candidates 2024 Feb 13 3(1):148–71.
19. Lavecchia A, Cerchia C. In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov Today. 2016 Feb;21(2):288-98.
20. Dara S, Dhamercherla S, Jadav SS, Babu CM, Ahsan MJ. Machine Learning in Drug Discovery: A Review. Artif Intell Rev. 2022;55(3):1947-1999.
21. Cao, R., Bhattacharya, D., Hou, J. et al. DeepQA: improving the estimation of single protein model quality with deep belief networks. BMC Bioinformatics 17, 495 (2016)
22. Spencer M, Eickholt J, Jianlin Cheng. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction. IEEE/ACM Trans Comput Biol Bioinform. 2015 Jan-Feb;12(1):103-12
23. Xue LC, Dobbs D, Alexandre M.J.J. Bonvin, Vasant Honavar. Computational prediction of protein interfaces: A review of data driven methods. FEBS Letters 2015 Oct 13;589(23):3516–26.
24. Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, Tiewsoh I, Lyngdoh NM, Das D, Bidarolli M, Sony HT. Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development. Int J Mol Sci. 2023 Jan 19;24(3).
25. Lim H, Fatma Cankara, Tsai CJ, Ozlem Keskin, Nussinov R, Attila Gursoy. Artificial intelligence approaches to human-microbiome protein–protein interactions. Current Opinion in Structural Biology 2022 Feb 10;73:102328–8.

26. Liu B, He H, Luo H, Zhang T, Jiang J. Artificial intelligence and big data facilitated targeted drug discovery. Stroke Vasc Neurol. 2019 Nov 7;4(4):206-213. doi: 10.1136/svn-2019-000290.
27.Kandathil SM, Lau AM, Jones DT. Machine learning methods for predicting protein structure from single sequences. Current Opinion in Structural Biology 2023 Jun 13 81:102627–7.
28. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature. 2021 Aug;596(7873):583-589. doi: 10.1038/s41586-021-03819-2.
29. Qureshi R, Irfan M, Gondal TM, Khan S, Wu J, Hadi MU, Heymach J, Le X, Yan H, Alam T. AI in drug discovery and its clinical relevance. Heliyon. 2023 Jul;9(7):e17575. doi: 10.1016/j.heliyon.2023.e17575.
30. Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical assessment of methods of protein structure prediction (CASP)-Round XIV. Proteins. 2021 Dec;89(12):1607-1617. doi: 10.1002/prot.26237.
31. Jing Y, Bian Y, Hu Z, Wang L, Xie XQ. Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era. AAPS J. 2018 Mar 30;20(3):58. doi: 10.1208/s12248-018-0210-0. Erratum in: AAPS J. 2018 Jun 25;20(4):79. doi: 10.1208/s12248-018-0243-4
32. Justin S. Smith, Adrian E. Roitberg, and Olexandr Isayev ACS Medicinal Chemistry Letters 2018 9 (11), 1065-1069
DOI: 10.1021/acsmedchemlett.8b00437
33. Narayanan RR. Impact of Artificial Intelligence (AI) on Drug Discovery and Product Development. Indian Journal of Pharmaceutical Education and Research 2022 56(3s):s387–97.
34. Powles J, Hodson H. Google DeepMind and healthcare in an age of algorithms. Health Technol (Berl). 2017;7(4):351-367. doi: 10.1007/s12553-017-0179-1..
35 Wan F, Zeng J. Deep learning with feature embedding for compound-protein interaction prediction. Biorxiv. 2016 Nov 7:086033.
36. Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, Wang J, Cong Q, Kinch LN, Schaeffer RD, Millán C, Park H, Adams C, Glassman CR, DeGiovanni A, Pereira JH, Rodrigues AV, van Dijk AA, Ebrecht AC, Opperman DJ, Sagmeister T, Buhlheller C, Pavkov-Keller T, Rathinaswamy MK, Dalwadi U, Yip CK, Burke JE, Garcia KC, Grishin NV, Adams PD, Read RJ, Baker D. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021 Aug 20;373(6557):871-876. doi: 10.1126/science.abj8754.
37. Gaudelet T, Day B, Jamasb AR, Soman J, Regep C, Liu G, Hayter JBR, Vickers R, Roberts C, Tang J, Roblin D, Blundell TL, Bronstein MM, Taylor-King JP. Utilizing graph machine learning within drug discovery and development. Brief Bioinform. 2021 Nov 5;22(6):bbab159. doi: 10.1093/bib/bbab159.
38. T. Baltrušaitis, C. Ahuja and L. -P. Morency, “Multimodal Machine Learning: A Survey and Taxonomy,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 2, pp. 423-443, 1 Feb. 2019, doi: 10.1109/TPAMI.2018.2798607
39.Han L, Liu Z, Jing Z, Liu Y, Chang H, Lei J, et al. Spatially resolved molecular and cellular atlas of the mouse brain. 2023 Dec 5; Available from: https://www.biorxiv.org/content/10.1101/2023.12.03.569501v1
40. Jimenes-Vargas, K., Pazos, A., Munteanu, C.R. et al. Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy. J Cheminform 16, 27 (2024). https://doi.org/10.1186/s13321-024-00816-1.
41.Abbas G, Rassam A, Karamshahi F, Rehab Abunora, Maha Abouseada. The Role of AI in Drug Discovery. ChemBioChem 2024 May 13 25(14). Available from: https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cbic.202300816

42. Dhakal A, McKay C, Tanner JJ, Cheng J. Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions. Brief Bioinform. 2022 Jan 17;23(1):bbab476. doi: 10.1093/bib/bbab476.
43. Qiu X, Li H, Ver Steeg G, Godzik A. Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development. Biomolecules. 2024; 14(3):339. https://doi.org/10.3390/biom14030339
44. Borkakoti N, Thornton JM. AlphaFold2 protein structure prediction: Implications for drug discovery. Curr Opin Struct Biol. 2023 Feb;78:102526. doi: 10.1016/j.sbi.2022.102526.
45. Liu Z, Wu T, Xiang G, Wang H, Wang B, Feng Z, Mu Y, Li K. Enhancing Animal Disease Resistance, Production Efficiency, and Welfare through Precise Genome Editing. Int J Mol Sci. 2022 Jun 30;23(13):7331. doi: 10.3390/ijms23137331.
46. Avorn J. The $2.6 billion pill–methodologic and policy considerations. N Engl J Med. 2015 May 14;372(20):1877-9. doi: 10.1056/NEJMp1500848.
47. Abbas G, Rassam A, Karamshahi F, Rehab Abunora, Maha Abouseada. The Role of AI in Drug Discovery. ChemBioChem 2024 May 13 25(14).
48. Tian K, Shao M, Wang Y, Guan J, Zhou S. Boosting compound-protein interaction prediction by deep learning. Methods. 2016 Nov 1;110:64-72. doi: 10.1016/j.ymeth.2016.06.024.
49. Wang, Z. H. You, X. Chen, S. X. Xia, F. Liu, X. Yan, Y. Zhou, K. J. Song, Journal of Computational Biology 2018, 25, 361–373. https://doi.org/10.1089/CMB.2017.0135/ASSET/IMAGES/LARGE/FIGURE8.JPEG.
50. Mayr A, Klambauer G, Unterthiner T, Steijaert M, Wegner JK, Ceulemans H, Clevert DA, Hochreiter S. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem Sci. 2018 Jun 6;9(24):5441-5451. doi: 10.1039/c8sc00148k
51. AlQuraishi M. End-to-End Differentiable Learning of Protein Structure. Cell Syst. 2019 Apr 24;8(4):292-301.e3. doi: 10.1016/j.cels.2019.03.006.
52. Current Opinion in Structural Biology | Journal | ScienceDirect.com by Elsevier Sciencedirect.com. 2024 Available from: https://www.sciencedirect.com/journal/current-opinion-in-structural-biology
53. Reddy AS, Zhang S. Polypharmacology: drug discovery for the future. Expert Rev Clin Pharmacol. 2013 Jan;6(1):41-7. doi: 10.1586/ecp.12.74. https://doi.org/10.1586/ECP.12.74.
54. Blaschke T, Arús-Pous J, Chen H, Margreitter C, Tyrchan C, Engkvist O, Papadopoulos K, Patronov A. REINVENT 2.0: An AI Tool for De Novo Drug Design. J Chem Inf Model. 2020 Dec 28;60(12):5918-5922. doi: 10.1021/acs.jcim.0c00915.
55. Pogány P, Arad N, Genway S, Pickett SD. De novo molecule design by translating from reduced graphs to SMILES. Journal of chemical information and modeling. 2018 Dec 11;59(3):1136-46.
56. Nelson A, Karageorgis G. Natural product-informed exploration of chemical space to enable bioactive molecular discovery. RSC Med Chem. 2020 Dec 16;12(3):353-362. doi: 10.1039/d0md00376j.
57. Lipinski C, Hopkins A. Navigating chemical space for biology and medicine. Nature. 2004 Dec 16;432(7019):855-61. doi: 10.1038/nature03193
58. Hessler G, Baringhaus KH. Artificial Intelligence in Drug Design. Molecules. 2018 Oct 2;23(10):2520. doi: 10.3390/molecules23102520.
59. Domenico A, Nicola G, Daniela T, et al.Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization. Journal of Chemical Information and Modeling. 2020 Oct;60(10):4582-4593. DOI: 10.1021/acs.jcim.0c00517.
60. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2018 Jun;23(6):1241-1250. doi: 10.1016/j.drudis.2018.01.039.. https://doi.org/10.1016/J.DRUDIS.2018.01.039.PubMedWeb of Science®Google Scholar
61. C. Agu, C. N. Obulose, Drug Dev Res 2024, 85, e22159. https://doi.org/10.1002/DDR.22159.
CASPubMedWeb of Science®Google Scholar
62. Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent Sci. 2018 Feb 28;4(2):268-276. doi: 10.1021/acscentsci.7b00572.
63. Merk D, Friedrich L, Grisoni F, Schneider G. De Novo Design of Bioactive Small Molecules by Artificial Intelligence. Mol Inform. 2018 Jan;37(1-2):1700153. doi: 10.1002/minf.201700153.
64. Segler MHS, Kogej T, Tyrchan C, Waller MP. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent Sci. 2018 Jan 24;4(1):120-131. doi: 10.1021/acscentsci.7b00512.
65. Klucznik T, Mikulak-Klucznik B, McCormack MP, Lima H, Szymkuć S, Manishabrata Bhowmick, et al. Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory. 2018 Mar 1 4(3):522–32.
66. Segler, M., Preuss, M. & Waller, M. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018). https://doi.org/10.1038/nature25978.
67.Schneider G, Clark DE. Automated De Novo Drug Design: Are We Nearly There Yet? Angewandte Chemie International Edition 2019 Feb 58(32):10792–803.
68. Popova M, Isayev O, Tropsha A. Deep reinforcement learning for de novo drug design. Sci Adv. 2018 Jul 25;4(7):eaap7885. doi: 10.1126/sciadv.aap7885.
69. Simon Z, Peragovics A, Vigh-Smeller M, et al. Drug effect prediction by polypharmacology-based interaction profiling. Journal of Chemical Information and Modeling. 2012 Jan;52(1):134-145. DOI: 10.1021/ci2002022.
70. Abbas G, Rassam A, Karamshahi F, Rehab Abunora, Maha Abouseada. The Role of AI in Drug Discovery. ChemBioChem 2024 May 13 25(14).
71.Rayka M, Firouzi R. GB‐score: Minimally designed machine learning scoring function based on distance‐weighted interatomic contact features. Molecular Informatics 2023 Feb 42(3).
72. Ryu JY, Kim HU, Lee SY. Deep learning improves prediction of drug-drug and drug-food interactions. Proc Natl Acad Sci U S A. 2018 May 1;115(18):E4304-E4311. doi: 10.1073/pnas.1803294115.
73. Struble TJ, Alvarez JC, Brown SP, Chytil M, Cisar J, DesJarlais RL, Engkvist O, Frank SA, Greve DR, Griffin DJ, Hou X, Johannes JW, Kreatsoulas C, Lahue B, Mathea M, Mogk G, Nicolaou CA, Palmer AD, Price DJ, Robinson RI, Salentin S, Xing L, Jaakkola T, Green WH, Barzilay R, Coley CW, Jensen KF. Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis. J Med Chem. 2020 Aug 27;63(16):8667-8682. doi: 10.1021/acs.jmedchem.9b02120.
74. Abou Hajal A, Al Meslamani AZ. Insights into artificial intelligence utilisation in drug discovery. J Med Econ. 2024 Jan-Dec;27(1):304-308. doi: 10.1080/13696998.2024.2315864.
75. Rai, M.; Paudel, N.; Sakhrie, M.; Gemmati, D.; Khan, I.A.; Tisato, V.; Kanase, A.; Schulz, A.; Singh, A.V. Perspective on Quantitative Structure–Toxicity Relationship (QSTR) Models to Predict Hepatic Biotransformation of Xenobiotics. Livers 2023, 3, 448-462. https://doi.org/10.3390/livers3030032
76. Abbas G, Rassam A, Karamshahi F, Rehab Abunora, Maha Abouseada. The Role of AI in Drug Discovery. ChemBioChem 2024 May 25(14). Available from: https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cbic.202300816

77. Jiang Y, Yu Y, Kong M, Mei Y, Yuan L, Huang Z, et al. Artificial Intelligence for Retrosynthesis Prediction. Engineering 2022 Aug 20 25:32–50.
78. Liu B, Ramsundar B, Kawthekar P, Shi J, Gomes J, Luu Nguyen Q, Ho S, Sloane J, Wender P, Pande V. Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models. ACS Cent Sci. 2017 Oct 25;3(10):1103-1113. doi: 10.1021/acscentsci.7b00303.
79. Litao Yan, Elena L. Glassman, and Tianyi Zhang. 2021. Visualizing Examples of Deep Neural Networks at Scale. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 313, 1–14. https://doi.org/10.1145/3411764.3445654
80. Segler MHS, Waller MP. Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. Chemistry. 2017 May 2;23(25):5966-5971. doi: 10.1002/chem.201605499.
81. Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018 Mar 28;555(7698):604-610
82. Szymkuć S, Gajewska EP, Klucznik T, Molga K, Dittwald P, Startek M, Bajczyk M, Grzybowski BA. Computer-Assisted Synthetic Planning: The End of the Beginning. Angew Chem Int Ed Engl. 2016 May 10;55(20):5904-37. doi: 10.1002/anie.201506101C
83. Lotfi Shahreza M, Ghadiri N, Mousavi SR, Varshosaz J, Green JR. A review of network-based approaches to drug repositioning. Brief Bioinform. 2018 Sep 28;19(5):878-892. doi: 10.1093/bib/bbx017.
84. Parvathaneni V, Kulkarni NS, Muth A, Gupta V. Drug repurposing: a promising tool to accelerate the drug discovery process. Drug Discov Today. 2019 Oct;24(10):2076-2085. doi: 10.1016/j.drudis.2019.06.014. https://doi.org/10.1016/J.DRUDIS.2019.06.014.CASPubMedWeb of Science®Google Scholar
85. Mohanty, M. Harun AI Rashid, M. Mridul, C. Mohanty, S. Swayamsiddha, Clinical Research & Reviews 2020, 14, 1027–1031. https://doi.org/10.1016/J.DSX.2020.06.068.Google Scholar
86. Yadav S, Singh A, Singhal R, Yadav JP. Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry. Intelligent Pharmacy 2024 Feb 24 ;2(3):367–80. Available
87. M. Corsello, J. A. Bittker, Z. Liu, J. Gould, P. McCarren, J. E. Hirschman, S. E. Johnston, A. Vrcic, B. Wong, M. Khan, J. Asiedu, R. Narayan, C. C. Mader, A. Subramanian, T. R. Golub, Nature Medicine 2017 23, 405–408.
88. ChemBioChem 2024 Jul 15 25(14). Available from: https://chemistry europe.onlinelibrary.wiley.com/toc/14397633/2024/25/14
89.Lozano-Diez A, Zazo R, Toledano DT, Gonzalez-Rodriguez J. An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition. PLoS ONE 2017 Aug 10 12(8):e0182580–0

90. 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 Jul 5;13(7):2524-30. doi: 10.1021/acs.molpharmaceut.6b00248.
91. Mak KK, Wong YH, Pichika MR. Artificial intelligence in drug discovery and development. Drug discovery and evaluation: safety and pharmacokinetic assays. 2024 Oct 22:1461-98.
92. Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Molecular pharmaceutics. 2017 Sep 5;14(9):3098-104.

93. Ozerov IV, Lezhnina KV, Izumchenko E, Artemov AV, Medintsev S, Vanhaelen Q, Aliper A, Vijg J, Osipov AN, Labat I, West MD. In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development. Nature communications. 2016 Nov 16;7(1):13427.
94. Li H, Sze K, Lu G, Ballester PJ. Machine‐learning scoring functions for structure‐based drug lead optimization. Wiley Interdisciplinary Reviews Computational Molecular Science 2020 Feb 5;10(5).
95.X. Zeng, X., Xiang, H., Yu, L. et al. Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework. Nat Mach Intell 4, 1004–1016 (2022). https://doi.org/10.1038/s42256-022-00557-6
96. Mohammad, jafari R,Sheibani M, Nezamoleslami,S,Shayesteh S, Jand Y, Dehpour AR.Drug Repositioning: A Review. JIMC.2018;1(1):7-10.
97. Gatta G, van der Zwan JM, Casali PG, Siesling S, Dei Tos AP, Kunkler I, Otter R, Licitra L, Mallone S, Tavilla A, Trama A, Capocaccia R; RARECARE working group. Rare cancers are not so rare: the rare cancer burden in Europe. Eur J Cancer. 2011 Nov;47(17):2493-511. doi: 10.1016/j.ejca.2011.08.008.
98. Padhy BM, Gupta YK. Drug repositioning: re-investigating existing drugs for new therapeutic indications. J Postgrad Med. 2011 Apr-Jun;57(2):153-60. doi: 10.4103/0022-3859.81870.
99. Hainsworth AH, Elahi FM, Corriveau RA. An introduction to therapeutic approaches to vascular cognitive impairment. Cerebral Circulation – Cognition and Behavior. 2021 ;2:100033. DOI: 10.1016/j.cccb.2021.100033.
100. Assmus F, Driouichet al. Need for a Standardized Translational Drug Development Platform: Lessons Learned from the Repurposing of Drugs for COVID-19. Microorganisms. 2022 Aug 12;10(8):1639. doi: 10.3390/microorganisms10081639.101B. E. Bloom, Expert Opin Orphan Drugs 2016, 4, 1–4. https://doi.org/10.1517/21678707.2016.1120664.
CASWeb of Science®Google Scholar
102. Novac N. Challenges and opportunities of drug repositioning. Trends Pharmacol Sci. 2013 May;34(5):267-72. doi: 10.1016/j.tips.2013.03.004
103 Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004 Aug;3(8):673-83. doi: 10.1038/nrd1468.
104.Tripathi MK, Nath A, Singh TP, Ethayathulla AS, Kaur P. Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. Molecular Diversity 2021 Jun 23 25(3):1439–60. Available from:

105.Murray CW, Erlanson DA, Hopkins AL, Keserü GM, Leeson PD, Rees DC, et al. Validity of Ligand Efficiency Metrics. ACS Medicinal Chemistry Letters.2014 May 9 5(6):616–8.
106.Kusumoto D, Yuasa S, Fukuda K. Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence. Pharmaceuticals. 2022 Apr 30 15(5):562–2.

107. Krishnan SR, Bung N, Vangala SR, Srinivasan R, Bulusu G, Roy A. De novo structure-based drug design using deep learning. Journal of chemical information and modeling. 2021 Nov 18;62(21):5100-9.
108.Lin JH, Perryman AL, Schames JR, McCammon JA. Computational Drug Design Accommodating Receptor Flexibility: The Relaxed Complex Scheme. Journal of the American Chemical Society 2002 Apr 26 124(20):5632–3.

109. Pereira JC, Caffarena ER, Dos Santos CN. Boosting Docking-Based Virtual Screening with Deep Learning. J Chem Inf Model. 2016 Dec 27;56(12):2495-2506. doi: 10.1021/acs.jcim.6b00355.
110. Wu, C., Gao, R., Zhang, Y. et al. PTPD: predicting therapeutic peptides by deep learning and word2vec. BMC Bioinformatics 20, 456 (2019). https://doi.org/10.1186/s12859-019-3006-z
111 . Malik MS, Ahmed SA, Althagafi II, Ansari MA, Kamal A. Application of triazoles as bioisosteres and linkers in the development of microtubule targeting agents. RSC Med Chem. 2020 Jan 29;11(3):327-348. doi: 10.1039/c9md00458k.
112. Elabar S. Review on Multitarget Drug Design Based on Computational Strategies for the Treatment of Alzheimer’s Disease. Alq J Med App Sci. 2021;4(2):181-190. https://doi.org/10.5281/zenodo.5248127
113. Elbadawi M, McCoubrey LE, Gavins FKH, Ong JJ, Goyanes A, Gaisford S, Basit AW. Disrupting 3D printing of medicines with machine learning. Trends Pharmacol Sci. 2021 Sep;42(9):745-757. doi: 10.1016/j.tips.2021.06.002.
114. Huang X, Tang F, Hua Y, Li X. In silico prediction of drug‐induced ototoxicity using machine learning and deep learning methods. Chemical Biology & Drug Design 2021 May 20 98(2):248–57.
115G. Panapitiya G, Girard M, Hollas A, Sepulveda J, Murugesan V, Wang W, et al. Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction. ACS Omega 2022 Apr 25 7(18):15695–710.
116. Judson RS, et.al. Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor. Toxicol Sci. 2015 Nov;148(1):137-54.
117. Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev. 2019 Sep 25;119(18):10520-10594. doi: 10.1021/acs.chemrev.8b00728.
118. Singh S, Gupta H, Sharma P, Sahi S. Advances in Artificial Intelligence (AI)-assisted approaches in drug screening. Artificial Intelligence Chemistry 2023 Dec 19 2(1):100039–9.

119 Basile AO, Yahi A, Tatonetti NP. Artificial Intelligence for Drug Toxicity and Safety. Trends Pharmacol Sci. 2019 Sep;40(9):624-635. doi: 10.1016/j.tips.2019.07.005.
120. Rim KT. In silico prediction of toxicity and its applications for chemicals at work. Toxicol Environ Health Sci. 2020;12(3):191-202. doi: 10.1007/s13530-020-00056-4.
121 Raies AB, Bajic VB. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci. 2016 Mar;6(2):147-172. doi: 10.1002/wcms.1240.
122 Mayr, G. Klambauer, T. Unterthiner, S. Hochreiter, Front Environ Sci 2016, 3, 167215. https://doi.org/10.3389/FENVS.2015.00080/BIBTEX.Google Scholar
123. Jain S, Siramshetty VB, Alves VM, Muratov EN, Kleinstreuer N, Tropsha A, Nicklaus MC, Simeonov A, Zakharov AV. Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods. J Chem Inf Model. 2021 Feb 22;61(2):653-663. doi: 10.1021/acs.jcim.0c01164. Epub 2021 Feb 3.
124 Lysenko A, Sharma A, Boroevich KA, Tsunoda T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci Alliance. 2018 Nov 28;1(6):e201800098. doi: 10.26508/lsa.201800098
125 Hasselgren C, Myatt GJ. Computational toxicology and drug discovery. Computational Toxicology: Methods and Protocols. 2018:233-44.
126 Vo AH, Van Vleet TR, Gupta RR, Liguori MJ, Rao MS. An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation. Chem Res Toxicol. 2020 Jan 21;33(1):20-37. doi: 10.1021/acs.chemrestox.9b00227.
127 Patlewicz G, Fitzpatrick JM. Current and future perspectives on the development, evaluation, and application of in silico approaches for predicting toxicity. Chemical research in toxicology. 2016 Apr 18;29(4):438-51.
128 Algamal ZY, Lee MH, Al‐Fakih AM, Aziz M. High‐dimensional QSAR prediction of anticancer potency of imidazo [4, 5‐b] pyridine derivatives using adjusted adaptive LASSO. Journal of Chemometrics. 2015 Oct;29(10):547-56.
129 Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom-Ackermann Z, Tran VM, Chiappino-Pepe A, Badran AH, Andrews IW, Chory EJ, Church GM, Brown ED, Jaakkola TS, Barzilay R, Collins JJ. A Deep Learning Approach to Antibiotic Discovery. Cell. 2020 Feb 20;180(4):688-702.e13. doi: 10.1016/j.cell.2020.01.021. Erratum in: Cell. 2020 Apr 16;181(2):475-483. doi: 10.1016/j.cell.2020.04.001.
130 Hakime Öztürk, Arzucan Özgür, Elif Ozkirimli, DeepDTA: deep drug–target binding affinity prediction, Bioinformatics, Volume 34, Issue 17, September 2018, Pages i821–i829, https://doi.org/10.1093/bioinformatics/bty593
131.Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, et al. Deep-Learning-Based Drug–Target Interaction Prediction. Journal of Proteome Research 2017 Mar 6 16(4):1401–9.
132 Karimi M, Wu D, Wang Z, Shen Y. DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics. 2019 Sep 15;35(18):3329-3338. doi: 10.1093/bioinformatics/btz111
133L. Pu L, Naderi M, Liu T, Wu HC, Mukhopadhyay S, Brylinski M. eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol. 2019 Jan 8;20(1):2. doi: 10.1186/s40360-018-0282-6.
134. Parvatikar PP, Patil S, Khaparkhuntikar K, Patil S, Singh PK, Sahana R, Kulkarni RV, Raghu AV. Artificial intelligence: Machine learning approach for screening large database and drug discovery. Antiviral Res. 2023 Dec;220:105740. doi: 10.1016/j.antiviral.2023.105740.
135. Chauhan H, Bernick J, Prasad D, Masand V. The role of artificial neural networks on target validation in drug discovery and development. InArtificial Neural Network for Drug Design, Delivery and Disposition 2016 Jan 1 (pp. 15-27). Academic Press.
136.Firoozeh Piroozmand, Fatemeh Mohammadipanah, Hedieh Sajedi. Spectrum of deep learning algorithms in drug discovery. Chemical Biology & Drug Design 2020 Sep 1 96(3):886–901.

137. Cheirdaris DG. Artificial neural networks in computer-aided drug design: An overview of recent advances. GeNeDis 2018: Computational Biology and Bioinformatics. 2020:115-25.
138 Içten E, Reklaitis GV, Nagy ZK. Advanced control for the continuous dropwise additive manufacturing of pharmaceutical products. InComputer Aided Chemical Engineering 2018 Jan 1 (Vol. 41, pp. 379-401). Elsevier.
139 Silvestrini S, Lavagna M. Deep learning and artificial neural networks for spacecraft dynamics, navigation and control. Drones. 2022 Sep 22;6(10):270.
140 Fernández-Delgado M, Sirsat MS, Cernadas E, Alawadi S, Barro S, Febrero-Bande M. An extensive experimental survey of regression methods. Neural Networks. 2019 Mar 1;111:11-34.
141 Das MK, Chakraborty T. ANN in pharmaceutical product and process development. InArtificial neural network for drug design, delivery and disposition 2016 Jan 1 (pp. 277-293). Academic Press.
142 Daina A, Zoete V. A boiled‐egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem. 2016 Jun 6;11(11):1117-21.
143 Olawale F, Iwaloye O, Elekofehinti OO. Virtual screening of natural compounds as selective inhibitors of polo-like kinase-1 at C-terminal polo box and N-terminal catalytic domain. Journal of Biomolecular Structure and Dynamics. 2022 Dec 26;40(24):13606-24.
144F. Ghasemi F, Mehridehnavi A, Pérez-Garrido A, Pérez-Sánchez H. Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks. Drug discovery today. 2018 Oct 1;23(10):1784-90.
145 Dahl GE, Jaitly N, Salakhutdinov R. Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231. 2014 Jun 4.
146 Tealab A, Hefny H, Badr A. Forecasting of nonlinear time series using ANN. Future Computing and Informatics Journal. 2017 Jun 1;2(1):39-47.
147 Bielecki A, Bielecki A. Foundations of artificial neural networks. Models of Neurons and Perceptrons: Selected Problems and Challenges. 2019:15-28.
149 Martinelli DD. Generative machine learning for de novo drug discovery: A systematic review. Computers in Biology and Medicine. 2022 Jun 1;145:105403.
150 Lavecchia A. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug discovery today. 2019 Oct 1;24(10):2017-32.
151 Nichols JA, Herbert Chan HW, Baker MA. Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophysical reviews. 2019 Feb 7;11:111-8.
152 Valueva MV, Nagornov NN, Lyakhov PA, Valuev GV, Chervyakov NI. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and computers in simulation. 2020 Nov 1;177:232-43.
153 Askr H, Elgeldawi E, Aboul Ella H, Elshaier YA, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artificial Intelligence Review. 2023 Jul;56(7):5975-6037.
154 Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN. 2018 IEEE winter conference on applications of computer vision (WACV). Grad‐CAM++: Generalized Gradient‐Based Visual Explanations for Deep Convolutional Networks. 2018:839-47.
155 Goh GB, Siegel C, Vishnu A, Hodas NO, Baker N. Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models. arXiv preprint arXiv:1706.06689. 2017 Jun 20.
156. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. InProceedings of the IEEE conference on computer vision and pattern recognition 2015 (pp. 1-9).
157. Vaz JM, Balaji S. Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics. Mol Divers. 2021 Aug;25(3):1569-1584. doi: 10.1007/s11030-021-10225-3.
158. Dhami DS, Yan S, Kunapuli G, Page D, Natarajan S. Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks. arXiv preprint arXiv:1911.06356. 2019 Nov 14.
159. Altalib MK, Salim N. Similarity-based virtual screen using enhanced Siamese multi-layer perceptron. Molecules. 2021 Nov 3;26(21):6669.
160. Venkatesan R, Li B. Convolutional neural networks in visual computing: a concise guide. CRC Press; 2017 Oct 23.
161. Leelananda, S. P.; Lindert, S. Beilstein J. Org. Chem. 2016, 12, 2694–2718. doi:10.3762/bjoc.12.267
162. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015 May 28;521(7553):436-44.
163. Sakhamuri MR, Henna S, Creedon L, Meehan K. Graph Modelling and Graph-Attention Neural Network for Immune Response Prediction. In2023 34th Irish Signals and Systems Conference (ISSC) 2023 Jun 13 (pp. 1-6). IEEE.
164. Liggieri L, Miller R. Editorial Overview: Hot Topic: COVID-19: Colloid and Interface Aspects of COVID-19. Current Opinion in Colloid & Interface Science. 2021 Dec;56:101525.
165. Senthilkumar V, Prabhu C. Optimization of design and development of a biomass gasifier–a review. Biofuels. 2024 Mar 6:1-9.
166. Ly HB, Vu HLT, Ho LS, Pham BT. Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm. Frontiers of Structural and Civil Engineering 2022 Feb 1;16(2):224–38.
167. Jiang H, He Y, Liao M, Jing Y, Zhang C. English-Vietnamese machine translation model based on sequence to sequence algorithm. In2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) 2020 Jun 12 (pp. 1086-1091). IEEE.
168. Devi KJ, Singh NH, Thongam K. Automatic speaker recognition from speech signals using self organizing feature map and hybrid neural network. Microprocessors and Microsystems. 2020 Nov 1;79:103264.
169. Rajamanickam R, Baskaran D. Neural network model for biological waste management systems. InCurrent Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering 2022 Jan 1 (pp. 393-415). Academic Press.
170. Zhou Y, Zhang Y, Lian X, Li F, Wang C, Zhu F, Qiu Y, Chen Y. Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic acids research. 2022 Jan 7;50(D1):D1398-407.
171. Yang Z, Zeng X, Zhao Y, Chen R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduction and Targeted Therapy. 2023 Mar 14;8(1):115.
172 AlQuraishi M. AlphaFold2@ CASP14:“It feels like one’s child has left home.”. Some Thoughts on a Mysterious Universe. 2020.
173. Basit SA, Qureshi R, Musleh S, Guler R, Rahman MS, Biswas KH, Alam T. COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19. Frontiers in Public Health. 2023 Mar 6;11:1125917.
174. Agrawal PJ. Artificial intelligence in drug discovery and development. Journal of Pharmacovigilance. 2018;6(2):1000e173.
175. Kumar A, Tejaswini P, Nayak O, Kujur AD, Gupta R, Rajanand A, Sahu M. A survey on IBM watson and its services. InJournal of Physics: Conference Series 2022 May 1 (Vol. 2273, No. 1, p. 012022). IOP Publishing.
176. Ramsundar B, Eastman P, Walters P, Pande V. Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more. ” O’Reilly Media, Inc.”; 2019 Apr 10.
177. Li X, Sun Y, Zhou Z, Li J, Liu S, Chen L, Shi Y, Wang M, Zhu Z, Wang G, Lu Q. Deep Learning‐Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer’s Disease. Advanced Science. 2024 May;11(18):2308970.
178. Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Molecular diversity. 2021 Oct 23:1-21.
179. Taylor L, Nitschke G. Improving deep learning with generic data augmentation. In2018 IEEE symposium series on computational intelligence (SSCI) 2018 Nov 18 (pp. 1542-1547). IEEE.
180. Khosla C, Saini BS. Enhancing performance of deep learning models with different data augmentation techniques: A survey. In2020 International Conference on Intelligent Engineering and Management (ICIEM) 2020 Jun 17 (pp. 79-85). IEEE.
181. Stork C, Wagner J, Friedrich NO, de Bruyn Kops C, Šícho M, Kirchmair J. Hit Dexter: A Machine‐Learning Model for the Prediction of Frequent Hitters. ChemMedChem. 2018 Mar 20;13(6):564-71.
182. Ferreira LL, Andricopulo AD. ADMET modeling approaches in drug discovery. Drug discovery today. 2019 May 1;24(5):1157-65.
183. Chan HS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing drug discovery via artificial intelligence. Trends in pharmacological sciences. 2019 Aug 1;40(8):592-604.
184. Zhu H. Big data and artificial intelligence modeling for drug discovery. Annual review of pharmacology and toxicology. 2020 Jan 6;60(1):573-89.
185. Brown N. In silico medicinal chemistry: computational methods to support drug design. Royal Society of Chemistry; 2015 Oct 30.
186. Ciallella HL, Zhu H. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chemical research in toxicology. 2019 Mar 14;32(4):536-47.
187. Madushanka A, Laird E, Clark C, Kraka E. SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability. Journal of Chemical Information and Modeling. 2024 Aug 23;64(17):6799-813.
188. Biswas A, Kumari A, Gaikwad DS, Pandey DK. Revolutionizing biological science: The synergy of genomics in health, bioinformatics, agriculture, and artificial intelligence. OMICS: A Journal of Integrative Biology. 2023 Dec 1;27(12):550-69.
189. Kose, U., Gupta, D., & Chen, X. (Eds.). (2023). Explainable Artificial Intelligence for Biomedical Applications (1st ed.). River Publishers. https://doi.org/10.1201/9781032629353
190. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S. Applications of machine learning in drug discovery and development. Nature reviews Drug discovery. 2019 Jun;18(6):463-77.
191. Sanjeevi M, Hebbar PN, Aiswarya N, Rashmi S, Rahul CN, Mohan A, Jeyakanthan J, Sekar K. Methods and applications of machine learning in structure-based drug discovery. InAdvances in Protein Molecular and Structural Biology Methods 2022 Jan 1 (pp. 405-437). Academic Press.
192. Dornaika F, Bosaghzadeh A, Salmane H, Ruichek Y. Object categorization using adaptive graph-based semi-supervised learning. InHandbook of Neural Computation 2017 Jan 1 (pp. 167-179). Academic Press.
193. Nagarajan V, Mandarapu D, Kulkarni M. Rt-knns unbound: Using rt cores to accelerate unrestricted neighbor search. InProceedings of the 37th International Conference on Supercomputing 2023 Jun 21 (pp. 289-300).
194. Rezaei N, Jabbari P. Practice examples. InImmunoinformatics of Cancers 2022 (pp. 223-261). Elsevier.
195. Ozturk Kiyak E, Ghasemkhani B, Birant D. High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis. Electronics. 2023 Sep 10;12(18):3828.
196. Badillo S, Banfai B, Birzele F, Davydov II, Hutchinson L, Kam‐Thong T, Siebourg‐Polster J, Steiert B, Zhang JD. An introduction to machine learning. Clinical pharmacology & therapeutics. 2020 Apr;107(4):871-85.
197. Siddalingappa R, Kanagaraj S. K-nearest-neighbor algorithm to predict the survival time and classification of various stages of oral cancer: a machine learning approach. F1000Research. 2023 Nov 16;11:70.
198. Mendes N, Vieira JG, Mano AP. Risk management in aviation maintenance: A systematic literature review. Safety science. 2022 Sep 1;153:105810.
199. Sikelis K, Tsekouras GE, Kotis K. Ontology-based feature selection: A survey. Future Internet. 2021 Jun 18;13(6):158.200J. Partanen, Environ. Plan. B Urban Anal. City Sci 2019, 47, 403–320.Google Scholar
201. Bichindaritz I, Marling C. Case-based reasoning in the health sciences: What’s next?. Artificial intelligence in medicine. 2006 Feb 1;36(2):127-35.
202. Wakiru JM, Pintelon L, Muchiri PN, Chemweno PK. A review on lubricant condition monitoring information analysis for maintenance decision support. Mechanical systems and signal processing. 2019 Mar 1;118:108-32.
203. Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine learning methods in drug discovery. Molecules. 2020 Nov 12;25(22):5277.
204. Yoon HR, Balupuri A, Choi KE, Kang NS. Small molecule inhibitors of DYRK1A identified by computational and experimental approaches. International journal of molecular sciences. 2020 Sep 17;21(18):6826.
205. Beetel RJ, Chettipally U, Dash R, Shankar S. Artificial intelligence and machine learning in clinical trial design and application. InA Handbook of Artificial Intelligence in Drug Delivery 2023 Jan 1 (pp. 571-580). Academic Press.
206. Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends in pharmacological sciences. 2019 Aug 1;40(8):577-91.
207. Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. Nature biotechnology. 2014 Jan;32(1):40-51.
208. Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemporary clinical trials communications. 2018 Sep 1;11:156-64.
209. Freedman DH. Hunting for new drugs with AI. Nature. 2019 Dec 1;576(7787):S49-53.
210. Lin A, Giuliano CJ, Palladino A, John KM, Abramowicz C, Yuan ML, Sausville EL, Lukow DA, Liu L, Chait AR, Galluzzo ZC. Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Science translational medicine. 2019 Sep 11;11(509):eaaw8412.
211. Ganesan A, Coote ML, Barakat K. Molecular dynamics-driven drug discovery: leaping forward with confidence. Drug discovery today. 2017 Feb 1;22(2):249-69.
212. Wang Y, Ribeiro JM, Tiwary P. Machine learning approaches for analyzing and enhancing molecular dynamics simulations. Current opinion in structural biology. 2020 Apr 1;61:139-45.
213. Veljkovic V, Loiseau PM, Figadere B, Glisic S, Veljkovic N, Perovic VR, Cavanaugh DP, Branch DR. Virtual screen for repurposing approved and experimental drugs for candidate inhibitors of EBOLA virus infection. F1000Research. 2015;4.
214. Bannigan P, Aldeghi M, Bao Z, Häse F, Aspuru-Guzik A, Allen C. Machine learning directed drug formulation development. Advanced Drug Delivery Reviews. 2021 Aug 1;175:113806.
215. Chen R, Liu X, Jin S, Lin J, Liu J. Machine learning for drug-target interaction prediction. Molecules. 2018 Aug 31;23(9):2208.
216. Jiménez-Luna J, Grisoni F, Schneider G. Drug discovery with explainable artificial intelligence. Nature Machine Intelligence. 2020 Oct;2(10):573-84.
217. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC medicine. 2019 Dec;17:1-9.
218. Juwara L, El-Hussuna A, El Emam K. An evaluation of synthetic data augmentation for mitigating covariate bias in health data. Patterns. 2024 Apr 12;5(4).
219. Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MD, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. Wiley Interdisciplinary Reviews: Computational Molecular Science. 2021 Sep;11(5):e1516.
220.Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow Jr RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M. Rethinking drug design in the artificial intelligence era. Nature reviews drug discovery. 2020 May;19(5):353-64.

Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 14/11/2024
Accepted 27/11/2024
Published 28/12/2024
233