Artificial Intelligence and Its Role In The Development Of Personalized Medicine And Drug Control

Artificial Intelligence and Its Role In The Development Of Personalized Medicine And Drug Control


  • Prof.Dr. Johan Waden Department of Computer science, University of Helsinki ,Norway



Artificial Intelligence, AI in Personalized Medicine, Drug Control, Drug Discovery using AI


DNA sequencing, imaging procedures, and wireless healthcare monitoring devices are all examples of high-throughput, data-intensive precision medicine assays and technologies that have necessitated new methods for analysing, integrating, and interpreting the enormous volumes of data they produce. While several statistical approaches have been developed to deal with the "big data" generated by such tests, previous experience with artificial intelligence (AI) techniques suggests that they may be especially well-suited. Furthermore, data-intensive biomedical technologies applied to study have shown that people differ greatly at the genetic, biochemical, physiological, exposure, and behavioural levels, particularly with regards to disease processes and treatment receptivity. This indicates the need to 'personalise' medications so that they better suit the complex and often individual needs of each patient. AI can play a significant role in the clinical research and development of new personalised health products, from selecting relevant contribute to sustainable to testing their utility, because of the importance of data-intensive assays in revealing appropriate intervention objectives and approaches for personalising medicines. The work here presents a variety of ways in which AI can contribute to the progress of personalised medicine, and we argue that the success of this endeavour is critically dependent on the improvement of appropriate assays and methods for storing, aggregating, accessing, and ultimately combining the data they generate. In addition, the manuscript also discusses the potential future research directions and highlights the shortcomings of various AI methods.


M. Segler, M. Preuss, and M. P. Waller, “Planning chemical syntheses with deep neural networks and symbolic AI,” Nature, vol. 555, no. 7698, pp. 604–610, 2018.

D. T. Ahneman, “Predicting reaction performance in C-N cross-coupling using machine learning,” Science, vol. 360, no. 6385, pp. 186–190, 2018.

A. Radovic, “Machine learning at the energy and intensity frontiers of particle physics,” Nature, vol. 560, no. 7716, pp. 41–48, 2018.

D. Silver, “Mastering the game of Go without human knowledge,” Nature, vol. 550, no. 7676, pp. 354–359, 2017.

N. S. Madhukar, “A New Big-Data Paradigm For Target Identification And Drug Discovery,” BioRxiv, 2018.

C. B. Butte and A. J, “Leveraging big data to transform target selection and drug discovery,” Clin Pharmacol Ther, vol. 99, no. 3, pp. 285–97, 2016.

Y. Hu and J. Bajorath, “Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited,” Future Sci OA, vol. 3, no. 2, pp. 179–179, 2017.

D. Hernandez, “How Robots Are Making Better Drugs, Faster,” Wall Street Journal, 2018.

“Patient-centered drug manufacture,” Nat Biotechnol, vol. 35, no. 6, pp. 485–485, 2017.

H. Schellekens, “Making individualized drugs a reality,” Nat Biotechnol, vol. 35, no. 6, pp. 507–513, 2017.

A. Mukhopadhyay, J. Sumner, L. H. Ling, R. H. C. Quek, A. T. H. Tan, G. G. Teng, . . Motani, and M, “Personalised Dosing Using the CURATE. AI Algorithm: Protocol for a Feasibility Study in Patients with Hypertension and Type II Diabetes Mellitus,” International Journal of Environmental Research and Public Health, vol. 19, no. 15, pp. 8979–8979, 2022.

A. Blasiak, A. T. Truong, A. Remus, L. Hooi, S. G. K. Seah, P. Wang, . . Ho, and D, “The IDentif. AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens,” NPJ digital medicine, vol. 5, no. 1, pp. 1–12, 2022.

S. Vadapalli, H. Abdelhalim, S. Zeeshan, and Z. Ahmed, “Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine,” Briefings in bioinformatics, 2022.

B. Lin and S. Wu, “Digital transformation in personalized medicine with artificial intelligence and the internet of medical things,” OMICS: A Journal of Integrative Biology, vol. 26, no. 2, pp. 77–81, 2022.







How to Cite

Prof.Dr. Johan Waden, “Artificial Intelligence and Its Role In The Development Of Personalized Medicine And Drug Control: Artificial Intelligence and Its Role In The Development Of Personalized Medicine And Drug Control”, WJCMS, vol. 1, no. 4, pp. 126–133, Dec. 2022, doi: 10.31185/wjcm.85.