A Review: Drug Discovery Methods Based on Artificial Intelligence
DOI:
https://doi.org/10.61173/khh85h64Keywords:
molecular docking, drug discovery, artificial intelligenceAbstract
The discovery of drugs is recognized as a lengthy, highly costly, and extremely complex process. For example, some traditional drug discovery methods consist of millions of trials to get a druggable compound to the market. Drug discovery based on artificial intelligence can be a prompt, low-cost, and effective way to streamline drug discovery. Although some works have been proposed to use artificial intelligence tools for drug discovery, few people summarize these advances in a systematic way. In this paper, we propose an organized and comprehensive review that outlines a broad range of appliances of artificial intelligence in drug discovery including harnessing virtual screening and molecular docking techniques, utilizing pathway networks for repurposing existing drugs, lead identification, biomarker research, identification of the target, diverse variety of artificial intelligence and their comparison, etc. In addition, we shed light on predicted limitations and challenges in drug discovery based on artificial intelligence, as well as sketch the strategies to harness its potential for upcoming drug design endeavors.
References
[1] Pushkaran A C, Arabi A A. From understanding diseases to drug design: can artificial intelligence bridge the gap? Artificial Intelligence Review, 2024, 57(4): 1
[2] Patel U K, Anwar A, Saleem S, et al. Artificial intelligence is an emerging technology in the current care of neurological disorders. Journal of Neurology, 2019, 268(5): 1623–1642.
[3] Nilsson N J. Artificial intelligence: A modern approach. Artificial Intelligence, 1996, 82(1–2): 369–380.
[4] 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. Medicinal Research Reviews, 2020, 41(3): 1427– 1473.
[5] Haenssle H A, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Annals of Oncology, 2018, 29(8): 1836–1842.
[6] Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115–118.
[7] Brock, G. N., Mukhopadhyay, P., Pihur, V., Webb, C., Greene, R. M., & Pisano, M. M. MmPalateMiRNA, an R package compendium illustrating the analysis of miRNA microarray data. Source code for biology and medicine, 2013, 8(1): 1.
[8] Vemula D, Jayasurya P, Sushmitha V, et al. CADD, AI, and ML in drug discovery: A comprehensive review. European Journal of Pharmaceutical Sciences, 2023, 181(2): 106324.
[9] Bijral R K, Singh I, Manhas J, et al. Exploring Artificial Intelligence in Drug Discovery: A Comprehensive Review. Archives of Computational Methods in Engineering, 2021, 29(4): 2513–2529.
[10] Turing A. Computing machinery and intelligence. Mind, 1950, LIX (236): 433–460.
[11] Talele T, Khedkar S, Rigby A. Successful Applications of Computer Aided Drug Discovery: Moving Drugs from Concept to the Clinic. Current Topics in Medicinal Chemistry, 2010, 10(1): 127–141.
[12] Sun X, Jia X, Lu Z, et al. Drug repositioning with adaptive graph convolutional networks. Bioinformatics, 2023, 40(1): 1-10.
[13] Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 2019, 18(6): 463–477.
[14] Gómez-Bombarelli R, Wei J N, Duvenaud D, et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Science, 2018, 4(2): 268–276.
[15] Basu T, Engel-Wolf S, Menzer O. The Ethics of Machine Learning in Medical Sciences: Where Do We Stand Today? Indian J Dermatol. 2020, 65(5): 358-364.
[16] Minh D, Wang H X, Li Y F, et al. Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 2021, 55(5): 3503–3568.
[17] Kannappan B, R M N J, N S, et al. Recent Advances of Artificial Intelligence in Drug Discovery Process. 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC), 2022, 245-249
[18] Murugan N A, Priya G R, Sastry G N, et al. Artificial intelligence in virtual screening: Models versus experiments. Drug Discovery Today, 2022, 27(7): 1913–1923.
[19] Carpenter K A, Cohen D S, Jarrell J T, et al. Deep learning and virtual drug screening. Future Medicinal Chemistry, 2018, 10(21): 2557–2567.
[20] Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science, 2018, 361(6400): 360–365.
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