AI Assisted Drug Discovery: Emphasizing Its Role in Accelerating Precision Medicine Initiatives and Improving Treatment Outcomes
Mohan Raparthi
Published 15-07-2022
Keywords
- AI,
- drug discovery,
- precision medicine,
- machine learning,
- deep learning
- molecular design,
- target identification,
- virtual screening,
- personalized therapy,
- healthcare ...More
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Abstract
Artificial Intelligence (AI) has emerged as a transformative tool in the field of drug discovery, revolutionizing the way researchers identify and develop new therapeutic compounds. This paper explores the application of AI in drug discovery processes, emphasizing its role in accelerating precision medicine initiatives and improving treatment outcomes. By leveraging AI algorithms, researchers can analyze vast amounts of biological data, predict drug-target interactions, and design novel molecules with enhanced specificity and efficacy. AI-driven approaches such as machine learning, deep learning, and natural language processing have enabled the discovery of new drug candidates in a fraction of the time and cost compared to traditional methods. This paper highlights key AI techniques and applications in drug discovery, discusses challenges and limitations, and examines future prospects for AI-driven precision medicine.
References
- Angermueller C, Pärnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol. 2016 Aug 1;12(7):878. doi: 10.15252/msb.20156651.
- Gawehn E, Hiss JA, Schneider G. Deep learning in drug discovery. Mol Inform. 2016 Dec;35(1):3-14. doi: 10.1002/minf.201501008.
- Jain S, Goyal AK, Gupta PN. Progress in artificial intelligence (AI)-enabled drug discovery. Artif Intell Med. 2021 May;116:102049. doi: 10.1016/j.artmed.2020.102049.
- Jiménez-Luna J, Ribeiro AJM, Nascimento ADS, Costa FMD, Vesely C, Domingues I, Gallardo-Alvarado J, Pires das Neves R, Giráldez T, Godinho Ferreira M, Vercauteren DP. Artificial intelligence in drug discovery: Recent advances and future perspectives. Expert Opin Drug Discov. 2018 Jun;13(6):505-514. doi: 10.1080/17460441.2018.1473783.
- Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, Zhavoronkov A. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 2017 Jun 20;8(7):10883-10890. doi: 10.18632/oncotarget.14073.
- Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J. PubChem substance and compound databases. Nucleic Acids Res. 2016 Jan 4;44(D1):D1202-D1213. doi: 10.1093/nar/gkv951.
- Schneider G. Automating drug discovery. Nat Rev Drug Discov. 2018 May;17(2):97-113. doi: 10.1038/nrd.2017.232.
- Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom-Ackerman Z, Tran VM. A deep learning approach to antibiotic discovery. Cell. 2020 Jun 25;180(4):688-702. doi: 10.1016/j.cell.2020.01.021.
- Wei W, Liu H, Li J, Mei J, Chen M, Luo C, Liang Y, Zhao G, Lu J. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug Discov Today. 2020 Sep;25(9):1710-1723. doi: 10.1016/j.drudis.2020.05.003.
- Xue W, Zeng J, Zhang W, Liu H, Liu H, Zhu F, Yang X, Xu C, Li J, Chen YZ, Luo H. Deep learning-based drug discovery for GPCR-targeted agents. Brief Bioinform. 2021 Jan 20;22(1):324-337. doi: 10.1093/bib/bbaa235.