Advances of AI-driven Drug Design and Discovery in Pharmaceuticals - Review
Dev Ras Pandey 1*, Sushree Sasmita Dash 1, Akanksha Mishra 1
Journal of Angiotherapy 8(1) 1-10 https://doi.org/10.25163/angiotherapy.819488
Submitted: 28 November 2023 Revised: 21 January 2024 Published: 25 January 2024
Abstract
The field of drug design and discovery is undergoing a transformative shift, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to expedite and optimize the drug development process. Traditional methods are often costly and time-consuming, involving extensive testing and sequential stages. However, contemporary drug development integrates AI, particularly in drug identification and preclinical studies, resulting in significant resource and time savings. AI is utilized for bioactivity and physicochemical forecasting, de novo molecule design, synthesis prediction, and drug-target profile representation. This review introduces the AI-based Drug Design and Discovery System (AI-D3S), a comprehensive approach utilizing serialization, de-serialization, Particle Swarm Optimization (PSO), and Support Vector Machine (SVM). The system demonstrates superior accuracy, precision, sensitivity, and specificity compared to conventional methods, showcasing an average improvement of 8.7%. Our review study evaluates the system on two chemical databases, MAO and Biodegradation, and illustrates its efficacy in predicting drug-annotation combinations. The potential of AI in pharmaceuticals extends from drug design to personalized therapies, decision-making, and efficient resource allocation in marketing. The research envisions AI as an indispensable tool in the pharmaceutical sector, driving innovation, reducing costs, and ensuring the production of higher-quality products. The AI-D3S model presented in this study sets the stage for future advancements in drug design and discovery, offering a promising avenue for the integration of AI in revolutionizing the pharmaceutical industry.
Keywords: Drug Design, Artificial Intelligence, Machine Learning, AI-Driven, Drug Development, Pharmaceuticals
References
Arabi, A. A. (2021). Artificial intelligence in drug design: algorithms, applications, challenges and ethics. Future Drug Discovery, 3(2), FDD59.
https://doi.org/10.4155/fdd-2020-0028
Brown, N., Ertl, P., Lewis, R., Luksch, T., Reker, D., & Schneider, N. (2020). Artificial intelligence in chemistry and drug design. Journal of Computer-Aided Molecular Design, 34, 709-715.
https://doi.org/10.1007/s10822-020-00317-x
Caban, M., Stepnowski, P. (2021). How to decrease pharmaceuticals in the environment? A review. Environ. Chem. Lett. 19, 3115-3138.
https://doi.org/10.1007/s10311-021-01194-y
Duch, W., Swaminathan, K., & Meller, J. (2007). Artificial intelligence approaches for rational drug design and discovery. Current pharmaceutical design, 13(14), 1497-1508.
https://doi.org/10.2174/138161207780765954
Guedeney, N., Cornu, M., Schwalen, F., Kieffer, C., Voisin-Chiret, A.S. (2023). PROTAC technology: A new drug design for chemical biology with many challenges in drug discovery. Drug Discov. Today. 28(1), 103395.
https://doi.org/10.1016/j.drudis.2022.103395
Guedes, I.A., Barreto, A.M., Marinho, D., Krempser, E., Kuenemann, M.A., Sperandio, O., Miteva, M.A. (2021). New machine learning and physics-based scoring functions for drug discovery. Sci. Rep. 11(1), 3198.
https://doi.org/10.1038/s41598-021-82410-1
Hessler, G., & Baringhaus, K. H. (2018). Artificial intelligence in drug design. Molecules, 23(10), 2520.
https://doi.org/10.3390/molecules23102520
Kolluri, S., Lin, J., Liu, R., Zhang, Y., Zhang, W. (2022). Machine learning and artificial intelligence in pharmaceutical research and development: a review. Arch Aesthetic Plast Surg. 24, 1-10.
https://doi.org/10.1208/s12248-021-00644-3
Kolluri, S., Lin, J., Liu, R., Zhang, Y., Zhang, W. (2022). Machine learning and artificial intelligence in pharmaceutical research and development: a review. Arch Aesthetic Plast Surg. 24, 1-10.
https://doi.org/10.1208/s12248-021-00644-3
Korshunova, M., Ginsburg, B., Tropsha, A., Isayev, O. (2021). OpenChem: a deep learning toolkit for computational chemistry and drug design. J. Chem. Inf. Model. 61(1), 7-13.
https://doi.org/10.1021/acs.jcim.0c00971
Kulkov, I. (2021). The role of artificial intelligence in business transformation: A case of pharmaceutical companies. Technol. Soc. 66, 101629.
https://doi.org/10.1016/j.techsoc.2021.101629
Lambrinidis, G., Tsantili-Kakoulidou, A. (2021). Multi-objective optimization methods in novel drug design. Expert Opin Drug Discov. 16(6), 647-658.
https://doi.org/10.1080/17460441.2021.1867095
McComb, M., Bies, R., Ramanathan, M. (2022). Machine learning in pharmacometrics: Opportunities and challenges. Br. J. Clin. Pharmacol. 88(4), 1482-1499.
https://doi.org/10.1111/bcp.14801
Mehta, S. The emerging roles of artificial intelligence in chemistry and drug design. In Data-Driven Technologies and Artificial Intelligence in Supply Chain. 158-173. CRC Press.
https://doi.org/10.1201/9781003462163-9
Mei, S., Zhang, K. (2021). A machine learning framework for predicting drug-drug interactions. Sci. Rep. 11(1), 17619.
https://doi.org/10.1038/s41598-021-97193-8
Mirmozaffari, M., Yazdani, R., Shadkam, E., Khalili, S.M., Mahjoob, M., Boskabadi, A. (2022). An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic. Sustainable Operations and Computers, 3, 156-167.
https://doi.org/10.1016/j.susoc.2022.01.003
Pasrija, P., Jha, P., Upadhyaya, P., Khan, M., & Chopra, M. (2022). Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery. Current Topics in Medicinal Chemistry, 22(20), 1692-1727.
https://doi.org/10.2174/1568026622666220701091339
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug discovery today, 26(1), 80.
https://doi.org/10.1016/j.drudis.2020.10.010
Ren, P.X., Shang, W.J., Yin, W.C., Ge, H., Wang, L., Zhang, X.L., Bai, F. (2022). A multi-targeting drug design strategy for identifying potent anti-SARS-CoV-2 inhibitors. Acta Pharmacol. Sin. 43(2), 483-493.
https://doi.org/10.1038/s41401-021-00668-7
Sun, Q., Gong, T., Liu, M., Ren, S., Yang, H., Zeng, S., Xu, H. (2022). Shikonin, a naphthalene ingredient: Therapeutic actions, pharmacokinetics, toxicology, clinical trials, and pharmaceutical research. Phytomedicine. 94, 153805.
https://doi.org/10.1016/j.phymed.2021.153805
Vora, L. K., Gholap, A. D., Jetha, K., Thakur, R. R. S., Solanki, H. K., & Chavda, V. P. (2023). Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics, 15(7), 1916.
https://doi.org/10.3390/pharmaceutics15071916
Wang, L., Ding, J., Pan, L., Cao, D., Jiang, H., & Ding, X. (2019). Artificial intelligence facilitates drug design in the big data era. Chemometrics and Intelligent Laboratory Systems, 194, 103850.
https://doi.org/10.1016/j.chemolab.2019.103850
Xiong, J., Xiong, Z., Chen, K., Jiang, H., Zheng, M. (2021). Graph neural networks for automated de novo drug design. Drug Discov. Today. 26(6), 1382-1393.
https://doi.org/10.1016/j.drudis.2021.02.011
Zeng, X., Wang, F., Luo, Y., Kang, S.G., Tang, J., Lightstone, F.C., Cheng, F. (2022). Deep generative molecular design reshapes drug discovery. Cell Rep. Med.
https://doi.org/10.1016/j.xcrm.2022.100794
View Dimensions
View Altmetric
Save
Citation
View
Share