Artificial Intelligence and Drug Discovery

Artificial Intelligence-based solutions make effective contributions to every area of ​​life. The healthcare field, in particular, has always had strong relationships with Artificial Intelligence, as it is one of the critical areas at the focal point of technology. In this context, Deep Learning, the current level reached by Artificial Intelligence, stands out with its landmark solutions in healthcare applications – even before the end of the first quarter of the twenty-first century. In particular, high-performance disease detection, robot-assisted surgeries, early cancer diagnosis and drug discovery solutions that have fueled human-machine comparisons have brought Deep Learning solutions in healthcare to sensational levels (Douissard et al., 2019; Huang et al., 2023; Li et al., 2019; Morris et al., 2023; Nag et al., 2022; Shen et al., 2019). Among these solutions, drug discovery studies, which are at the center of the fight against diseases, have gained more momentum, especially in recent years. Dynamic diseases, the need for rapid and effective results in drug development, economic reasons, and even lessons learned from the COVID-19 outbreak have been the catalysts for drug discovery research with Deep Learning (Bano et al., 2023; Floresta et al., 2022; Kumar et al., 2022; Luo et al., 2022; Subbiah, 2023).

The discovery and development of a new drug is known to be a very costly process that spans years (Chan et al., 2019; Çelik et al., 2021). Studies report that drug discovery R&D expenditures are up to 2.8 billion US dollars on average and the entire development process takes an average of 12-14 years (DiMasi et al., 2016; Lim, 2023; Sarkar et al., 2023; Wouters et al., 2020; Chang et al., 2023). Obtaining a usable drug requires consideration of all kinds of obstacles in biological life, possible differences, interactions between drug components, and a large number of clinical trials that are not always successful (Bruno et al., 2019; Lo et al., 2019). At this point, Deep Learning, specific to Artificial Intelligence, has become a tool that provides superiority over traditional solutions in terms of time, cost and success (Chen et al., 2018; Çelik et al., 2021; Schneider et al., 2020). Thanks to Deep Learning techniques, more effective analysis of patterns in data leading to drug discovery is provided; thus, vaccine development processes are supported (Yang et al., 2021), and even remarkable developments such as solving the protein structure prediction problem (Service, 2020) are experienced. As a result, there is a transition towards Artificial Intelligence applications in pharmaceutical companies, and it is evaluated that Artificial Intelligence, which is expected to have a market of 5 billion US dollars by 2024, will play a role in reshaping the field of health and pharmacology (Sarkar et al., 2023).

References

• Bano, I., Butt, U. D., Mohsan, S. A. H. 2023. “New challenges in drug discovery”. In Novel Platforms for Drug Delivery Applications (pp. 619-643). Woodhead Publishing.
• Bruno, A., Costantino, G., Sartori, L., Radi, M. 2019. “The in silico drug discovery toolbox: applications in lead discovery and optimization”. Current Medicinal Chemistry, 26(21), 3838-3873.
• Chan, H. S., Shan, H., Dahoun, T., Vogel, H., Yuan, S. 2019. “Advancing drug discovery via artificial intelligence”. Trends in Pharmacological Sciences, 40(8), 592-604.
• Chang, Y., Hawkins, B. A., Du, J. J., Groundwater, P. W., Hibbs, D. E., Lai, F. 2023. “A Guide to In Silico Drug Design”. Pharmaceutics, 15(1), 49.
• Çelik, İ. N., Arslan, F. K., Tunç, R., Yıldız, İ. 2021. “Drug discovery and artificial intelligence in its development (in Turkish)”. Journal of Faculty of Pharmacy of Ankara University, 45(2), 17-17.
• DiMasi, J. A., Grabowski, H. G., Hansen, R. W. 2016. “Innovation in the pharmaceutical industry: new estimates of R&D costs”. Journal of Health Economics, 47, 20-33.
• Douissard, J., Hagen, M. E., Morel, P. 2019. “The da Vinci surgical system”. Bariatric robotic surgery: a comprehensive guide, 13-27.
• Floresta, G., Zagni, C., Gentile, D., Patamia, V., Rescifina, A. 2022. “Artificial Intelligence Technologies for COVID-19 De Novo Drug Design”. International Journal of Molecular Sciences, 23(6), 3261.
• Huang, R., Li, H., Suomi, R., Li, C., Peltoniemi, T. 2023. “Intelligent Physical Robots in Health Care: Systematic Literature Review”. Journal of Medical Internet Research, 25, e39786.
• Kumar, S. A., Ananda Kumar, T. D., Beeraka, N. M., Pujar, G. V., Singh, M., Narayana Akshatha, H. S., Bhagyalalitha, M. 2022. “Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry”. Future Medicinal Chemistry, 14(4), 245-270.
• Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., Gao, X. 2019. “Deep learning in bioinformatics: Introduction, application, and perspective in the big data era”. Methods, 166, 4-21.
• Lim, S. 2023. “The Process and Costs of Drug Development 2022”. FTLO Science. Çevrimiçi: https://ftloscience.com/process-costs-drug-development/ Accessed: 13 March 2023.
• Lo, Y. C., Ren, G., Honda, H., Davis, K. L. 2019. Artificial intelligence-based drug design and discovery. Cheminformatics and Its Applications. InTech Open.
• Luo, Y., Wunderink, R. G., Lloyd-Jones, D. 2022. “Proactive vs reactive machine learning in health care: lessons from the COVID-19 pandemic”. JAMA, 327(7), 623-624.
• Morris, M. X., Rajesh, A., Asaad, M., Hassan, A., Saadoun, R., Butler, C. E. 2023. “Deep learning applications in surgery: Current uses and future directions”. The American Surgeon, 89(1), 36-42.
• Nag, S., Baidya, A. T., Mandal, A., Mathew, A. T., Das, B., Devi, B., Kumar, R. 2022. “Deep learning tools for advancing drug discovery and development”. 3 Biotech, 12(5), 110.
• Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., …, Sony, H. T. 2023. “Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development”. International Journal of Molecular Sciences, 24(3), 2026.
• Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., …, Schneider, G. 2020. “Rethinking drug design in the artificial intelligence era”. Nature Reviews Drug Discovery, 19(5), 353-364.
• Service, R. F. 2020. “‘The game has changed.’AI triumphs at protein folding”. Science. 370(6521), 1144-1145.
• Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., Sieh, W. 2019. “Deep learning to improve breast cancer detection on screening mammography”. Scientific Reports, 9(1), 12495.
• Subbiah, V. 2023. “The next generation of evidence-based medicine”. Nature Medicine, 29, 49-58.
• Wouters, O. J., McKee, M., Luyten, J. 2020. “Estimated research and development investment needed to bring a new medicine to market, 2009-2018”. JAMA, 323(9), 844-853.
• Yang, Z., Bogdan, P., Nazarian, S. 2021. “An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study”. Scientific Reports, 11(1), 1-21.