Authors : Hossam Abdelrahman Al-Ansary

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/5cs7y9nu

Scribd : https://tinyurl.com/2bzssmum

DOI : https://doi.org/10.5281/zenodo.14930503

Abstract : An Artificial Intelligence (AI) is revolutionizing the pharmaceutical industry by enhancing and streamlining numerous processes, ranging from drug discovery to clinical trial optimization. AI technologies help accelerate processes, reduce costs, and improve effectiveness, enabling pharmaceutical companies to achieve long-term competitive advantages. This study aims to advance the pharmaceutical industry by designing a software solution that utilizes a genetic algorithm based on artificial intelligence. In this research, drug components are represented as genes within a set of chromosomes, allowing for the optimization of pharmaceutical molecule design by exploring vast spaces of possible chemical compounds. The proposed algorithm identifies the most effective molecules while minimizing potential side effects, significantly accelerating the drug discovery process by reducing the time and cost required to produce new or advanced formulations. This innovative approach holds the potential to transform drug development and improve outcomes in the pharmaceutical sector.

Keywords : Artificial Intelligence, Genetic Algorithm, Evolutionary Algorithm, Software Engineering, Knowledge Base.

References :

1. K Huanbutta a, K Burapapadh , P Kraisit , P Sriamornsak , K Suwanpitak ,Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance,European Journal of Pharmaceutical Sciences, 69:262–277, 2024
2. S. Bodade, N. Bajad Mam,  S. Deshmukh, S, Mangesh, Artificial Intelligence in Pharmaceutical Industry, International Journal of Advanced Research in Science, Communication and Technology (IJARSCT),Volume 3, Issue 3, December 2023.
3. P. Sajja and R. Akerkar, Knowledge-Based Systems for Development, Advanced Knowledge Based Systems: Model Applications Research, vol. 1, pp. 1-11, 2010.
4. W. Lo and J. Choobineh, Knowledge-Based Systems as Database Design Tools: A Comparative Study, Intelligent Support Systems Technology: Knowledge Management, 2002.
5. Mak, K.-K.; Pichika, M.R. Artificial Intelligence in Drug Development: Present Status and Future Prospects. Drug Discov. Today, 24, 773–780, 2019.
6. Scannell, J.W.; Blanckley, A.; Boldon, H.; Warrington, B. Diagnosing the Decline in Pharmaceutical R&D Efficiency. Nat. Rev. Drug Discover., 11, 191–200, 2012.
7. Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K. and Tekade, R.K., . Artificial intelligence in drug discovery and development. Drug discovery today, 26(1), p.80,2021.
8. In Bioinformatics Tools for Pharmaceutical Drug Product Development; John Wiley & Sons, Ltd.: Hoboken, NJ, USA,pp. 1–18, 2023.
9. Lalitkumar K. Vora , Amol D. Gholap  , Keshava Jetha , Raghu Raj Singh Thakur ,Hetvi K. Solanki  and Vivek P. Chavda,Artificial Intelligence in Pharmaceutical Technology and Drug, School of Pharmacy, Queen’s University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK© 2023 by the authors.Licensee MDPI.
10. Mafarja, M., Mirjalili, S., Aljarah, I., & Al-Zoubi, A. M. Hybridizing Genetic Algorithm with Advanced Metaheuristics for Optimization Problems. Swarm and Evolutionary Computation, 77, 101273. [DOI: 10.1016/j,2023.
11. Nguyen, T. T., Nguyen, M. T., & Vo, B. A Review on Genetic Algorithms and Their Applications in Optimization. Artificial Intelligence Review, 56(2), 1157-1190. [DOI: 10.1007/s10462-023-10412-5],2023.
12. Hossam Alansary,” A Proposed Genetic Algorithm Model to Improve Effort Estimation in Agile Software Development” International Journal of Computer Science and Information Security (IJCSIS), Vol. 22, No.4,2024
13. Ochoa, G., & Veerapen, N. Genetic Algorithms: Advances, Trends, and Future Directions. Evolutionary Computation, 31(1), 1-27. [DOI: 10.1162/evco_a_00364] 2023.
14. Cai, Z., Sun, Y., & Chen, J. Multi-Objective Genetic Algorithms: A Comprehensive Survey. IEEE Transactions on Evolutionary Computation, 27(5), 1054-1075. [DOI: 10.1109/TEVC..3273856], 2023.
15. Abd Elaziz, M., Oliva, D., & Hinojosa, S. Genetic Algorithms in Machine Learning: Applications and Challenges. Expert Systems with Applications, 235, 120312. [DOI: 10.1016/j.eswa.120312], 2024.
16. Kar, A. K. A Review of Evolutionary Algorithms for Optimization Problems: Trends and Future Directions." Applied Soft Computing, 139, 110931. [DOI:10.1016/j.asoc..110931], 2023.
17. Mirjalili, S., & Heidari, A. A. Advances in Evolutionary Computation: State-of-the-Art and Emerging Trends. Swarm and Evolutionary Computation, 80, 101290. [DOI:10.1016/j.swevo.101290], 2023.
18. Wu, G., & Luo, Q. ,A Survey on Hybrid Evolutionary Algorithms for Large-Scale Optimization." Information Sciences, 646, 119204. [DOI:10.1016/j.ins.2023.119204], 2024.
19. Hossam Alansary,” A Proposed Genetic Algorithm Model to Improve Effort Estimation in Agile Software Development” International Journal of Computer Science and Information Security (IJCSIS), Vol. 22, No.4,2024
20. Eldos, T. ,A, New Migration Model For Distributed Genetic Algorithms , Proceedings of the International Conference on Scientific Computing , , (CSC06), (pp. 128-134), 2006.
21. W Z Guo, L J Hao and W J. Di, Application and Development of Artificial Intelligence Technology for the Data Management and Analysis in Forestry[J], Artificial Intelligence Computational Intelligence. Aici. International Conferenc, vol. 4, no. 4, pp. 438-441, 2009.
22. M H Huang and R T. Rust, Artificial Intelligence in Service, Journal of Service Research, vol. 21, no. 2, pp. 155-172, 2018.
23. Verma and Archana, "A Survey on Image Contrast Enhancement Using Genetic Algorithm", International Journal of Scientific and Research Publications, vol. 2, no. 7, 1, pp. 2, July 2012.
24. Arkhipov DI, Wu D, Wu T, Regan AC, A parallel genetic algorithm framework for transportation planning and logistics management, IEEE Access 8:106506–106515, 2020.
25. Cheng H, Yang S, Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks. Applications of evolutionary computation, Springer, In, pp 562–571, 2010.
26. Das AK, Pratihar DK, A direction-based exponential mutation operator for real-coded genetic algorithm. IEEE International Conference on Emerging Applications of Information Technology, 2018.
27. Dash SR, Dehuri S, Rayaguru S,Discovering interesting rules from biological data using parallel genetic algorithm, 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad,, pp. 631–636, 2013.
28. de Ocampo ALP, Dadios EP , Energy cost optimization in irrigation system of smart farm by using genetic algorithm, 2017IEEE 9th international conference on humanoid. Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Manila, pp 1–7, 2017.
29. Deb K, Pratap A, Agarwal S, Meyarivan T , A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. 2002
30. Di Fatta G, Hoffmann F, Lo Re G, Urso A , A genetic algorithm for the design of a fuzzy controller for active queue management. IEEE Trans Syst Man Cybern Part C Appl Rev 33(3):313–324, 2003.
31. D-Manríquez, R Alvarado, B Zambrano, G Melendez, El Leal JC , An automatic document classifier system based on genetic algorithm and taxonomy. IEEE Access 6:21552–21559, 2018.
32. EkbataniFard GH, Monsefi R, Akbarzadeh-T M-R, Yaghmaee M et al. A multi-objective genetic algorithm based approach for energy efficient qos-routing in two-tiered wireless sensor net-works. In: wireless pervasive computing (ISWPC), 2010 5th IEEE international symposium on. IEEE, pp 80–85. 2010.
33. Shukla AK, Singh P, Vardhan M , A new hybrid feature subset selection framework based on binary genetic algorithm and information theory. International Journal of Computational Intelligence and Applications 18(3):1950020(1–10), 2019.