Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Design

Editor-in-Chief

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

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Editorial

Revolutionizing Drug Discovery: The Role of AI and Machine Learning

Author(s): Abhinav Vashishat, Ghanshyam Das Gupta and Balak Das Kurmi*

Volume 29, Issue 39, 2023

Published on: 11 December, 2023

Page: [3087 - 3088] Pages: 2

DOI: 10.2174/0113816128287941231206050340

Price: $65

Next »
[1]
Gupta Y, Savytskyi OV, Coban M, et al. Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeu-tics. Mol Aspects Med 2023; 91: 101151.
[http://dx.doi.org/10.1016/j.mam.2022.101151] [PMID: 36371228]
[2]
Rao TV, Gaddam A, Kurni M, Saritha K. Reliance on artificial intelligence, machine learning and deep learning in the era of industry 4.0. Smart healthcare system design: Security and privacy aspects 2022; 281-99.
[3]
Tambuyzer E, Vandendriessche B, Austin CP, et al. Therapies for rare diseases: Therapeutic modalities, progress and challenges ahead. Nat Rev Drug Discov 2020; 19(2): 93-111.
[http://dx.doi.org/10.1038/s41573-019-0049-9] [PMID: 31836861]
[4]
Aldewachi H, Al-Zidan RN, Conner MT, Salman MM. High-throughput screening platforms in the discovery of novel drugs for neuro-degenerative diseases. Bioengineering (Basel) 2021; 8(2): 30.
[http://dx.doi.org/10.3390/bioengineering8020030] [PMID: 33672148]
[5]
Landers RN, Behrend TS. Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models. Am Psychol 2023; 78(1): 36-49.
[http://dx.doi.org/10.1037/amp0000972] [PMID: 35157476]
[6]
Hartl D, de Luca V, Kostikova A, et al. Translational precision medicine: An industry perspective. J Transl Med 2021; 19(1): 245.
[http://dx.doi.org/10.1186/s12967-021-02910-6] [PMID: 34090480]
[7]
Tiwari PC, Pal R, Chaudhary MJ, Nath R. Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Dev Res 2023; ddr.22115.
[http://dx.doi.org/10.1002/ddr.22115] [PMID: 37712494]
[8]
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873): 583-9.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[9]
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]
[10]
Sinha K, Ghosh N, Sil PC. A review on the recent applications of deep learning in predictive drug toxicological studies. Chem Res Toxicol 2023; 36(8): 1174-205.
[http://dx.doi.org/10.1021/acs.chemrestox.2c00375] [PMID: 37561655]
[11]
Askr H, Elgeldawi E, Aboul Ella H, Elshaier YAMM, Gomaa MM, Hassanien AE. Deep learning in drug discovery: An integrative review and future challenges. Artif Intell Rev 2023; 56(7): 5975-6037.
[http://dx.doi.org/10.1007/s10462-022-10306-1] [PMID: 36415536]
[12]
Hristova VA, Chan DW. Cancer biomarker discovery and translation: Proteomics and beyond. Expert Rev Proteomics 2019; 16(2): 93-103.
[http://dx.doi.org/10.1080/14789450.2019.1559062] [PMID: 30556752]
[13]
Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial intelligence in pharmaceutical technology and drug deliv-ery design. Pharmaceutics 2023; 15(7): 1916.
[http://dx.doi.org/10.3390/pharmaceutics15071916] [PMID: 37514102]
[14]
Hassija V, Chamola V, Mahapatra A, et al. Interpreting black-box models: A review on explainable artificial intelligence. Cognit Comput 2023; 1-30.
[http://dx.doi.org/10.1007/s12559-023-10179-8]
[15]
Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Soc Sci Med 2022; 296: 114782.
[http://dx.doi.org/10.1016/j.socscimed.2022.114782] [PMID: 35152047]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy