Revolutionizing Drug Discovery: The Role of Artificial Intelligence in Modern Drug Design
Ravi Indla, Vijay Singh Baghel, Parth Soni, Neelesh Khuteta, Aniruddha Prajapati, Hemangi A Virani
Keywords :
Artificial Intelligence (AI), Deep Learning (DL), Natural Language Processing (NLP), Drug Repurposing, Lead Compound Optimization, Drug-Target Interactions, De Novo Drug Design
Citation Information :
Indla R, Baghel VS, Soni P, Khuteta N, Prajapati A, Virani HA. Revolutionizing Drug Discovery: The Role of Artificial Intelligence in Modern Drug Design. 2024; 1 (1):34-40.
The traditional drug discovery process is slow, expensive, and prone to high failure rates, with timelines of 10–15 years and costs reaching $1–2 billion. Recent advancements in artificial intelligence (AI) have revolutionized drug design by enabling the analysis of vast biomedical datasets, identifying patterns, and making predictions that streamline and optimize the drug discovery pipeline. This article explores the transformative role of AI methodologies, including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and generative models, in accelerating target identification, lead compound optimization, and predicting drug toxicity or efficacy. AI applications in drug repurposing, de novo drug design, and the prediction of drug-target interactions are discussed, showcasing significant reductions in time and resource requirements. The article also highlights critical challenges, such as data quality, model interpretability, and regulatory concerns, which must be addressed to fully realize the potential of AI in drug discovery. With continued advancements and collaboration between computational and pharmaceutical sciences, AI promises to revolutionize drug development, paving the way for personalized and precision medicine.
Wouters OJ, McKee M, Luyten J. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018 [published correction appears in JAMA. 2022 Sep 20;328(11):1110. doi: 10.1001/jama.2022.14317
Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics. 2023;15(7):1916. doi: 10.3390/pharmaceutics15071916
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. doi: 10.1016/j.drudis.2020.10.010
Kumar N, Srivastava R. Deep learning in structural bioinformatics: current applications and future perspectives. Brief Bioinform. 2024;25(3):bbae042. doi: 10.1093/bib/bbae042
Sun Y, Jiao Y, Shi C, Zhang Y. Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2. Comput Struct Biotechnol J. 2022;20:5014-5027. doi: 10.1016/j.csbj.2022.09.002
Agu PC, Afiukwa CA, Orji OU, et al. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Sci Rep. 2023;13(1):13398. Published 2023 Aug 17. doi: 10.1038/s41598-023-40160-2.
Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther. 2023;17:2691-2725. Published 2023 Sep 6. doi: 10.2147/DDDT.S424991
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;2(3):367-380. https://doi.org/10.1016/j.ipha.2024.02.009.
Meyers J, Fabian B, Brown N. De novo molecular design and generative models. Drug Discov Today. 2021;26(11):2707-2715. doi: 10.1016/j.drudis.2021.05.019
Vogt M. Using deep neural networks to explore chemical space. Expert Opin Drug Discov. 2022;17(3):297-304. doi: 10.1080/17460441.2022.2019704
Visan AI, Negut I. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life (Basel). 2024;14(2):233. Published 2024 Feb 7. doi: 10.3390/life14020233
Cortial L, Montero V, Tourlet S, Del Bano J, Blin O. Artificial intelligence in drug repurposing for rare diseases: a mini-review. Front Med (Lausanne). 2024;11:1404338. Published 2024 May 22. doi: 10.3389/fmed.2024.1404338
Wu K, Karapetyan E, Schloss J, Vadgama J, Wu Y. Advancements in small molecule drug design: A structural perspective. Drug Discov Today. 2023;28(10):103730. doi: 10.1016/j.drudis.2023.103730
Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146-157. doi: 10.2174/157340911795677602
Tran TTV, Tayara H, Chong KT. Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives. Pharmaceutics. 2023;15(4):1260. Published 2023 Apr 17. doi: 10.3390/pharmaceutics15041260
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):1427-1473. doi: 10.1002/med.21764
Huanbutta K, Burapapadh K, Kraisit P, et al. Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. Eur J Pharm Sci. 2024;203:106938. doi: 10.1016/j.ejps.2024.106938
Hassija V, Chamola V, Mahapatra A. Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cogn Comput 16, 45–74 (2024). https://doi.org/10.1007/s12559-023-10179-8
Oualikene-Gonin W, Jaulent MC, Thierry JP, et al. Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities. Front Pharmacol. 2024;15:1437167. Published 2024 Aug 2. doi: 10.3389/fphar.2024.1437167
Rehman AR, Li M, Wu B, Ali Y, Rasheed S, Shaheen S, Liu X, Luo R, Zhang J. Role of Artificial Intelligence in Revolutionizing Drug Discovery, Fundamental Research. 2024, doi: https://doi.org/10.1016/j.fmre.2024.04.021.
