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Artificial Intelligence Applications in Drug Discovery and Research

Year 2024, Volume: 4 Issue: 2, 87 - 96, 27.12.2024

Abstract

Existing drug treatments may be inadequate for all the old and new diseases that people face every day. Therefore, the discovery and development of new drugs is an inevitable necessity to protect human health and treat diseases. Discovering a new drug involves many steps, such as selecting the drug with therapeutic effect from a large number of active compounds, determining the ADMET properties of the drug, and conducting clinical studies to determine its toxicity and side effect profile. It’s a costly and time-consuming process. According to the California Biomedical Research Association, it takes an average of 12 years and $359 million to get a drug from the lab to the patient. With the increase in digitalization in the field of health, as in every field, scientists have resorted to artificial intelligence to solve the problem of cost and time. Pharmaceutical manufacturing companies have made major investments and developed numerous artificial intelligence-based algorithms to be used in different stages of drug discovery. With the use of these algorithms in drug discovery and research, the money and time spent has decreased and efficiency has increased. This mini-review discusses artificial intelligence applications in drug discovery and research.

References

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  • P Hassanzadeh, F Atyabi, R Dinarvand, “The significance of artificial intelligence in drug delivery system design”, Advanced drug delivery reviews, vol. 151–152, pp. 169-190, 2019.
  • LK Vora, AD Gholap, K Jetha, RRS Thakur, HK Solanki, “Artificial intelligence in pharmaceutical technology and drug delivery design”, Pharmaceutics, vol. 15(7), pp. 1916, 2023
  • AI Visan, I Negut, “Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery” Life, vol.14(2), pp.233, 2024.
  • S Yang, S Kar, “Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity”, Artificial Intelligence Chemistry, Vol. 1 (2), pp. 10001, 2023.
  • GL Martin, J Jouganous, R Savidan, A Bellec, “Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data”, Drug Safety, vol. 45, pp 535–548, 2022.
  • A Syrowatka, W Song, MG Amato, D Foer, “Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review”, The Lancet Digital Health,, vol. 4, Iss. 2, pp.137-e148, 2022.
Year 2024, Volume: 4 Issue: 2, 87 - 96, 27.12.2024

Abstract

References

  • A. N. Ramesh, C. Kambhampati, J. R. T. Monson, P. J. Drew “Artificial intelligence in medicine” Ann R Coll Surg Engl, Vol.86 no.5, Sep., pp.334–338, 2004.
  • M.K.Tripathi, A. Nath, T.P.Singh, A.S. Ethayathulla, P. Kaur, “Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery”, Molecular Diversity, vol. 25, no.3, pp. 1440-1446, 2021.
  • R. Gupta, D. Srivastava, M. Sahu, S. Tiwari, R.K. Ambasta, P. Kumar, “Artificial intelligence to deep learning: machine intelligence approach for drug discovery”, Molecular Diversity, vol.25, no.3, pp.1315-1360, 2021.
  • M Coşkun, Ö Yıldırım, A Uçar, Y Demır, “An Overview Of Popular Deep Learning Methods”, European Journal of Technique, Vol. 7, noç 2, pp. 165 – 176, 2017.
  • J. Yan, P. Bhadra, A. Li, P. Sethiya, L. Qin, H.K. Tai, K.H. Wong, S.W.I Siu, “Deep-AmPEP30: improve short antimicrobial peptides prediction with deep learning”, Molecular Therapy-Nucleic Acids, vol. 20, pp.882-894, 2020.
  • I. Ponzoni, V. Sebastián-Pérez, M.J. Martínez, C. Roc. “QSAR classification models for predicting the activity of inhibitors of beta-secretase (BACE1) associated with Alzheimer's disease” Scientific reports, vol. 9, Article number: 9102, 2019.
  • L. Xie, S. He, X. Song, X. Bo, Z. Zhang, “Deep learning-based transcriptome data classification for drug-target interaction prediction”, BMC genomics, vol. 19, article number 667, 2018.
  • İ. N. Çelik, F. K. Arslan, R. Tunç, İ. Yıldız, “İlaç Keşfi ve Geliştirilmesinde Yapay Zekâ”, Journal of Faculty of Pharmacy of Ankara University, vol. 45, Issue: 2, pp.400 - 427, 2021.
  • P. Agrawal, “Artificial Intelligence in Drug Discovery and Development”, Journal of
  • S Büyükgöze, E Dereli. “Dijital sağlık uygulamalarında yapay zeka”, VI. Uluslararası Bilimsel ve Mesleki Çalışmalar Kongresi-Fen ve Sağlık, no:4, 2019.
  • S. Hochreiter, G. Klambauer, M. Rarey, “Machine learning in drug discovery” Journal of Chemical Information and Modeling, vol. 58, Issue 9, pp. 1723 – 1724, 2018.
  • A. Daina, O. Michielin, V. Zoete, “SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules”, Scientific reports, 7, Article number: 42717, 2017.
  • Y Zhang, S Liang, Y Feng, Q Wang, F Sun, S Chen, “Automation of literature screening using machine learning in medical evidence synthesis: a diagnostic test accuracy systematic review protocol”, Systematic reviews, vol. 11, article number 11, 2022.
  • Y Feng, S Liang, Y Zhang, S Chen, “Automated medical literature screening using artificial intelligence: a systematic review and meta-analysis” Journal of the American Medical Informatics Association, vol. 29, 8, pp. 1425–1432, August 2022.
  • RSK Vijayan, J Kihlberg, JB Cross, V Poongavanam, “Enhancing preclinical drug discovery with artificial intelligence” Drug discovery today, vol. 27, Issue 4, 967-984, 2022.
  • A Khadela, S Popat, J Ajabiya, D Valu. “AI, ML and other bioinformatics tools for preclinical and clinical development of drug products, Bioinformatics Tools for Pharmaceutical Drug Product Development, Chapter. 12, 2023.
  • B Lewandowski, G De Bo, JW Ward, M Papmeye, “Sequence-Specific Peptide Synthesis by an Artificial Small-Molecule Machine”, Science, vol 339, 6116, pp. 189-193, 2013.
  • M Goles, A Daza, G Cabas-Mora, “Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides”, Briefings in Bioinformatics, Volume 25, Issue 4, July 2024.
  • EA Poweleit, AA Vinks, T Mizuno, “Artificial intelligence and machine learning approaches to facilitate therapeutic drug management and model-informed precision dosing” Therapeutic drug monitoring, 45(2):p 143-150, April 2023.
  • KK Mak, YH Wong, MR Pichika, “Artificial intelligence in drug discovery and development”, Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays, Springer Nature, pp 1461–1498.
  • KS Vidhya, A Sultana, N Kumar, H Rangareddy, “Artificial intelligence's impact on drug discovery and development from bench to bedside”, Cureus, vol. 22, 15(10), 2023.
  • P Hassanzadeh, F Atyabi, R Dinarvand, “The significance of artificial intelligence in drug delivery system design”, Advanced drug delivery reviews, vol. 151–152, pp. 169-190, 2019.
  • LK Vora, AD Gholap, K Jetha, RRS Thakur, HK Solanki, “Artificial intelligence in pharmaceutical technology and drug delivery design”, Pharmaceutics, vol. 15(7), pp. 1916, 2023
  • AI Visan, I Negut, “Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery” Life, vol.14(2), pp.233, 2024.
  • S Yang, S Kar, “Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity”, Artificial Intelligence Chemistry, Vol. 1 (2), pp. 10001, 2023.
  • GL Martin, J Jouganous, R Savidan, A Bellec, “Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data”, Drug Safety, vol. 45, pp 535–548, 2022.
  • A Syrowatka, W Song, MG Amato, D Foer, “Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review”, The Lancet Digital Health,, vol. 4, Iss. 2, pp.137-e148, 2022.
There are 27 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Reviews
Authors

Şeyma Mintaş This is me 0009-0000-0340-8552

Canan Sevimli Gür 0000-0002-2210-5925

Publication Date December 27, 2024
Submission Date November 18, 2024
Acceptance Date December 13, 2024
Published in Issue Year 2024 Volume: 4 Issue: 2

Cite

IEEE Ş. Mintaş and C. Sevimli Gür, “Artificial Intelligence Applications in Drug Discovery and Research”, Journal of Artificial Intelligence and Data Science, vol. 4, no. 2, pp. 87–96, 2024.

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