Ai In Pharmaceutical Innovation: A Review Of Drug Discovery And Clinical Trial Integration
Abstract
Artificial Intelligence has revolutionized the process of drug discovery and improved clinical trials, which lead to the reduction of time and cost. Our review highlights the role of Prescription AI in the evolution of drug development processes. The first quartile discusses QSAR modeling and binding free energy molecular docking, coupled with how these methods can be improved with AI. They are capable of predicting molecular interactions at astonishing levels of accuracy. Identifying and validating more effective drug targets with the help of AI. Utilizing machine learning and multi-omics approaches to discover novel therapies. It summarizes committee-overseen AI-driven molecular engineered drugs. For instance, drugs’ ADMET properties help design and screen lead drug candidates. AI has had a similar impact on all lines of clinical trials in consolidation. From the processes of patient specification & categorization, to novel visualization techniques. Moreover, there exists a paradigm of drug-device combinations which allows for novel drug development strategies. Untapped areas of marketed drugs and synergies in treatment may enhance efficacy. Data deficiencies, bias in algorithms and regulatory compliant mechanisms are barriers that must be addressed. This study emphasizes the efforts made to resolve the issues of ethical transparency. The review emphasises principles to stem the abuse of AI in pharmaceutical advancement. In this review, The AI is embedded across all phases of drug development process. From the inception of the drug to the testing of it on humans so as to the impact of AI on the future of healthcare. It creates a foundation for more advanced research and partnerships in pharmaceutical AI.
References
Huang, D., Yang, M., Wen, X., Xia, S., & Yuan, B. (2024). AI-Driven drug discovery: Accelerating the development of novel therapeutics in biopharmaceuticals. International Medical Science Research Journal, 4(8). https://doi.org/10.51594/imsrj.v4i8.1458
Ejeta, B. M., Das, M. K., Das, S., Bekere, F. F., & Tayeng, D. (2024). Transformative Role of Artificial Intelligence in the Pharmaceutical Sector. Journal of Angiotherapy. https://doi.org/10.25163/angiotherapy.899933
Kokudeva, M., Vichev, M., Naseva, E., Miteva, D., & Velikova, T. (2024). Artificial intelligence as a tool in drug discovery and development. World Journal of Experimental Medicine, 14(3). https://doi.org/10.5493/wjem.v14.i3.96042
Han, R., Yoon, H., Kim, G., Lee, H., & Lee, Y. (2023). Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals, 16(9). https://doi.org/10.3390/ph16091259
Wenteler, A., Cabrera, C. P., Wei, W., Neduva, V., & Barnes, M. R. (2024). AI approaches for the discovery and validation of drug targets. None. https://doi.org/10.1017/pcm.2024.4
Alanazi, A. A. A., Al Fahad, A. I. A., Almorshed, A. S. A., Alrbian, A. A. M., Alnughaymishi, A. A. S., Al-Mutairi, N. H. B., & Alajmi, A. A. (2022). Artificial intelligence in drug discovery: Current applications and future directions. International Journal of Health Sciences, 6(10). https://doi.org/10.53730/ijhs.v6ns10.15290
Deng, J., Yang, Z., Samaras, D., & Wang, F. (2021). Artificial Intelligence in Drug Discovery: Applications and Techniques. None. https://doi.org/10.1093/bib/bbab430
T, S., Kaliappan, S., Ali, H., & Vijay Kumar, B. (2024). AI - Driven Drug Discovery and Therapeutic Target Identification for Rare Genetic Diseases. None. https://doi.org/10.1109/ASSIC60049.2024.10507989
Matsuzaka, Y., & Yashiro, R. (2022). Applications of Deep Learning for Drug Discovery Systems with BigData. BioMedInformatics, 2(4). https://doi.org/10.3390/biomedinformatics2040039
Deepthimahanthi, Y. P., Balu, P., Shing, W. L., Krishnan, K., Manjunathan, J., Ashokkumar, K., Jayanthi, M., Suganthi, M., & Abirami, G. (2023). AI-Based Drug Design: Revolutionizing Drug Discovery through in Silico Analysis. None. https://doi.org/10.61453/intij.202356
McGibbon, M., Shave, S., Dong, J., Gao, Y., Houston, D. R., Xie, J., Yang, Y., Schwaller, P., & Blay, V. (2023). From intuition to AI: Evolution of small molecule representations in drug discovery. None. https://doi.org/10.1093/bib/bbad422
Walters, W., & Barzilay, R. (2021). Critical assessment of AI in drug discovery. Expert Opinion on Drug Discovery, 16(2), 91–101. https://doi.org/10.1080/17460441.2021.1915982
Vinayak, N. V., Lingolu, D., Kumari, D., & Kumar, P. (2024). Evaluating the Impact of AI and ML on Modern Drug Discovery. None. https://doi.org/10.69613/8tckqp18
Harrer, S., Shah, P., Antony, B., & Hu, J. (2019). Artificial Intelligence for Clinical Trial Design. Elsevier BV. https://doi.org/10.1016/j.tips.2019.05.005
Serrano, D., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G., Ramirez, B. I., Snchez-Guirales, S. A., Simon, J. A., Tomietto, G., Rapti, C., Ruiz, H. K., Rawat, S., Kumar, D., & Lalatsa, A. (2024). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics, 16(10). https://doi.org/10.3390/pharmaceutics16101328
Mohapatra, M., Sahu, C., & Mohapatra, S. (2024). Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives. Current Drug Targets. https://doi.org/10.2174/0113894501322734241008163304
Shaheen, M. Y. (2021). Applications of Artificial Intelligence (AI) in healthcare: A review. None. https://doi.org/10.14293/s2199-1006.1.sor-.ppvry8k.v1
Chen, D., Cao, C., Kloosterman, R., Parsa, R., & Raman, S. (2024). Trial Factors Associated With Completion of Clinical Trials Evaluating AI: Retrospective Case-Control Study. Journal of Medical Internet Research. https://doi.org/10.2196/58578
Visan, A., & Negu, I. (2024). Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. None. https://doi.org/10.3390/life14020233
Dhudum, R., Ganeshpurkar, A., & Pawar, A. (2024). Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications. None. https://doi.org/10.3390/ddc3010009
Sumathi, S., Suganya, K., Swathi, K., Sudha, B., Poornima, A., Varghese, C. A., & Aswathy, R. (2023). A Review on Deep Learning-Driven Drug Discovery: Strategies, Tools, and Applications. Current Pharmaceutical Design. https://doi.org/10.2174/1381612829666230412084137
Ashiwaju, B., Orikpete, O., & Uzougbo, C. (2023). The intersection of artificial intelligence and big data in drug discovery: A review of current trends and future implications. Matrix Science Pharma. https://doi.org/10.4103/mtsp.mtsp_14_23
23. Patil, P., Nrip, N. K., Hajare, A., Hajare, D. A., Patil, M. K., Kanthe, R., & Gaikwad, A. T. (2023). Artificial Intelligence and Tools in Pharmaceuticals: An Overview. Research Journal of Pharmacy and Technology. https://doi.org/10.52711/0974-360x.2023.00341
Protrka, N., & Abazi, B. (2024). Artificial Intelligence in Health Care: Various Applications. International Convention on Information and Communication Technology, Electronics and Microelectronics. https://doi.org/10.1109/MIPRO60963.2024.10569971
Blanco-González, A., Cabeza, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & García-Fandiño, R. (2023). The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/ph16060891
Hasselgren, C., & Oprea, T. I. (2023). Artificial Intelligence for Drug Discovery: Are We There Yet? Annual Reviews. https://doi.org/10.1146/annurev-pharmtox-040323-040828
Niazi, S. K. (2023). The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Design, Development, and Therapy. https://doi.org/10.2147/DDDT.S424991
Usmani, U. A., & Usmani, M. U. (2023). AI-Driven Biomedical and Health Informatics: Harnessing Artificial Intelligence for Improved Healthcare Solutions. None. https://doi.org/10.1109/INCOFT60753.2023.10425780
Nuthakki, S. K., & Siddhartha, K. (2023). AI and The Future of Medicine: Pioneering Drug Discovery with Language Models. International Journal of Science and Research (IJSR). https://doi.org/10.21275/sr24304173757
Singh, S., Kumar, R., Payra, S., & Singh, S. K. (2023). Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery. Cureus, Inc.. https://doi.org/10.7759/cureus.44359
Gabashvili, A. U., & Irene, S. (2023). Artificial Intelligence in Biomedicine: Systematic Review. medRxiv. https://doi.org/10.1101/2023.07.23.23292672
Kokudeva, M., Vichev, M., Naseva, E., Miteva, D., & Velikova, T. (2024). Artificial intelligence as a tool in drug discovery and development. World Journal of Experimental Medicine, 14(3), 960–976. https://doi.org/10.5493/wjem.v14.i3.96042
Burri, S. R., Diallo, M. Y., Sharma, L., & Dutt, V. (2023). AI-Driven Drug Discovery: Unravelling the Potential of Generative Adversarial Networks (GANs) in Pharmaceutical Research. None. https://doi.org/10.1109/ICTACS59847.2023.10390116
Alanazi, A. A. A., Al Fahad, A. I. A., Almorshed, A. S. A., Alrbian, A. A. M., Alnughaymishi, A. A. S., Al-Mutairi, N. H. B., Alajmi, A. A., & Al Otaibi, S. G. (2022). Artificial intelligence in drug discovery: Current applications and future directions. International Journal of Health Sciences, 6(10), 152–163. https://doi.org/10.53730/ijhs.v6ns10.15290
Zeb, S., Fnu, N., Abbasi, N., & Fahad, M. (2024). AI in Healthcare: Revolutionizing Diagnosis and Therapy. None. https://doi.org/10.47709/ijmdsa.v3i3.4546
Askr, H., Elgeldawi, E., & Ella, A. E. (2023). Deep learning in drug discovery: An integrative review and future challenges. Artificial Intelligence Review, 56, 5975–6037. https://doi.org/10.1007/s10462-022-10306-13
T., B., & Mansoor, Z. J. (2021). Using drug–drug and protein-protein similarities as feature vectors for drug–target binding prediction. Chemometrics and Intelligent Laboratory Systems, 217, 104405. https://doi.org/10.1016/j.chemolab.2021.104405
Tropsha, A., Isayev, O., Varnek, A., Schneider, G., & Cherkasov, A. (2024). Integrating QSAR modeling and deep learning in drug discovery: The emergence of deep QSAR. Nature Reviews Drug Discovery, 23(2), 141–155. https://doi.org/10.1038/s41573-023-00832-0
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021). Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Molecular Diversity, 25(3), 1315–1360. https://doi.org/10.1007/s11030-021-10217
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M. M., & Zhang, B. (2021). Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Medical Research Reviews, 41(5), 1427–1473. https://doi.org/10.1002/med.21764
Dhamodharan, G., & Mohan, C. G. (2022). Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer’s disease. Molecular Diversity, 26(2), 175–183. https://doi.org/10.1007/s11030-021-10282
Zhou, Y., Wang, F., Tang, J., Nussinov, R., & Cheng, F. (2020). Artificial intelligence in COVID-19 drug repurposing. The Lancet Digital Health, 2(11), e667–e676. https://doi.org/10.1016/S2589-7500(20)30192-8
Verma, N., Qu, X., Trozzi, F., Elsaied, M., Karki, N., Tao, Y., Zoltowski, B., Larson, E. C., & Kraka, E. (2021). Predicting potential SARS-CoV-2 drugs: In-depth drug database screening using deep neural network framework SSNet, classical virtual screening, and docking. International Journal of Molecular Sciences, 22(4), 1392. https://doi.org/10.3390/ijms22041573
Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A. (2021). De novo design of new chemical entities for SARS-CoV-2 using artificial intelligence. Future Medicinal Chemistry, 13(6), 575–585. https://doi.org/10.4155/fmc-2020-0262
Lv, H., Shi, L., Berkenpas, J. W., Dao, F. Y., Zulfiqar, H., Ding, H., Zhang, Y., Yang, L., & Cao, R. (2021). Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design. Briefings in Bioinformatics, 22(6), bbab320. https://doi.org/10.1093/bib/bbab320
