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Abstract

Artificial intelligence (AI) is transforming drug development by improving precision, decreasing timelines and costs, and enabling AI-powered drug design. This paper examines current advances in deep generative models (DGMs) for de novo drug creation, including various techniques and their tremendous influence. It critically examines the issues that are inextricably linked to these technologies, providing methods to realize their full potential. It includes case studies of both triumphs and failures in moving medicines to clinical trials using AI. Finally, it presents a forwardlooking strategy for optimizing, DGMs in de novo drug design, resulting in speedier and more cost-effective drug development.

Keywords

Artificial intelligence (AI), Drug Discovery, improving precision, decreasing timelines and costs, and enabling AI-powered drug design

Introduction

Drug Discovery   

In recent years, there has been a lot of interest in medicinal chemistry's application of artificial intelligence (AI) as a potential way to transform the pharmaceutical sector.  [1] The process of finding and creating new drugs, or drug discovery, is a difficult and drawn-out undertaking that has historically relied on time-consuming methods like high-throughput screening and trial-and-error testing.  However, by making it possible to analyze vast volumes of data more accurately and efficiently, artificial intelligence (AI) techniques like machine learning (ML) and natural language processing have the potential to speed up and enhance this process [2]. The scientists recently revealed the successful application of deep learning (DL) to accurately predict the potency of medicinal molecules.  [3] . The toxicity of potential medications has also been predicted by AIbased techniques [4].  These and other studies have demonstrated AI's potential to increase the efficacy and efficiency of drug discovery procedures.  But there are drawbacks and restrictions to using AI to create novel bioactive chemicals.  To completely comprehend the benefits and limitations of AI in this field, more research is required, and ethical considerations must be taken into account.  Notwithstanding these obstacles, it is anticipated that AI will play a major role in the creation of novel drugs and treatments during the coming years. [6].

Reference

  1. Vamathevan, J., Clark, D., Czodrowski, P., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477. https://doi.org/10.1038/s41573-019-0024-5   
  2. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250.  https://doi.org/10.1016/j.drudis.2018.01.039   
  3. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x   
  4. Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016). DeepTox: Toxicity prediction using deep learning. Frontiers in Environmental Science, 3, 80.           https://doi.org/10.3389/fenvs.2015.00080   
  5. Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 310. https://doi.org/10.1186/s12911-020-01332-6   
  6. Walters, W. P., & Murcko, M. A. (2020). Assessing the impact of generative AI on medicinal chemistry. Nature Biotechnology, 38, 143–145.  https://doi.org/10.1038/s41587-019-0361-2   
  7. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039   
  8. Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., ... & Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688–702.e13. https://doi.org/10.1016/j.cell.2020.01.021   
  9. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., ... & Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x   
  10. Mak, K.-K., & Pichika, M. R. (2019). Artificial intelligence in drug development: Present status and future prospects. Drug Discovery Today, 24(3), 773–780. https://doi.org/10.1016/j.drudis.2018.11.014   
  11. Zhou, J., Wang, Q., Pan, S., & Du, X. (2020). Artificial intelligence in COVID-19 drug repurposing. The Lancet Digital Health, 2(12), e667–e676. https://doi.org/10.1016/S25897500(20)30223-4   
  12. Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016). Boosting dockingbased virtual screening with deep learning. Journal of Chemical Information and Modeling, 56(12), 2495– 2506. https://doi.org/10.1021/acs.jcim.6b00340   
  13. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039   
  14. Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., ... & Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 15(141), 20170387. https://doi.org/10.1098/rsif.2017.0387   
  15. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., ... & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2   
  16. Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., ... & Kavukcuoglu, K. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706–710. https://doi.org/10.1038/s41586019-1923-7   
  17. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., ... & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477.   https://doi.org/10.1038/s41573-019-0024-5   
  18. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., ... & Aspuru-Guzik, A. (2019). Deep learning enables rapid   
  19. Callaway, E. (2020). ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature, 588(7837), 203–204. https://doi.org/10.1038/d41586020-03348-4  
  20. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., ... & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2  
  21. Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., ... & Kavukcuoglu, K. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706–710. https://doi.org/10.1038/s41586019-1923-7  
  22. Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., ?ídek, A., ... & Hassabis, D. (2021). Highly accurate protein structure prediction for the human proteome. Nature, 596(7873), 590–596. https://doi.org/10.1038/s41586-021-03828-1  
  23. Anighoro, A., Bajorath, J., & Rastelli, G. (2014). Polypharmacology: Challenges and opportunities in drug discovery. Journal of Medicinal Chemistry, 57(19), 7874–7887. https://doi.org/10.1021/jm5006463 
  24. Bocci, G., Cassetta, L., & Gozzi, G. (2017). In silico prediction of off-targets for improved drug safety. Frontiers in Pharmacology, 8, 280. https://doi.org/10.3389/fphar.2017.00280 
  25. Hopkins, A. L. (2008). Network pharmacology: The next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690. https://doi.org/10.1038/nchembio.118 
  26. Pushpakom, S., Iorio, F., Eyers, P. A., et al. (2019). Drug repurposing: Progress, challenges and recommendations. Nature Reviews Drug Discovery, 18(1), 41–58. https://doi.org/10.1038/nrd.2018.168 
  27. Ryu, J. Y., Kim, H. U., & Lee, S. Y. (2018). Deep learning improves prediction of drug– drug and drug–target interactions. Proceedings of the National Academy of Sciences, 115(18), E4304–E4311. https://doi.org/10.1073/pnas.1803294115 
  28. Sterling, T., & Irwin, J. J. (2015). ZINC 15 – Ligand discovery for everyone. Journal of Chemical Information and Modeling, 55(11), 2324–2337. https://doi.org/10.1021/acs.jcim.5b00559 
  29. Wishart, D. S., Feunang, Y. D., Guo, A. C., et al. (2018). DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082. https://doi.org/10.1093/nar/gkx1037 
  30. Xiong, G., Luo, Y., Ji, W., et al. (2024). Artificial intelligence for polypharmacology and multi-target drug discovery. International Journal of Molecular Sciences, 25(14), 6996. https://doi.org/10.3390/ijms25146996 
  31. Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019). Autonomous discovery in the chemical sciences part II: Outlook. Accounts of Chemical Research, 53(5), 895–906. https://doi.org/10.1021/acs.accounts.9b00640 
  32. Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., & Lee, A. A. (2020). Machine intelligence for chemical reaction prediction. Chemical Science, 11(2), 331– 339. https://doi.org/10.1039/C9SC05704H 
  33. Segler, M. H. S., Preuss, M., & Waller, M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698), 604–610. https://doi.org/10.1038/nature25978 
  34. 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. https://doi.org/10.1016/j.jhealeco.2016.01.012 
  35. Harrer, S., Shah, P., Antony, B., & Hu, J. (2019). Artificial intelligence for clinical trial design. Trends in Pharmacological Sciences, 40(8), 577–591. https://doi.org/10.1016/j.tips.2019.06.004 
  36. Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2 
  37. Wallach, I., Dzamba, M., & Heifets, A. (2015). AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv preprint arXiv:1510.02855. 
  38. Wong, C. H., Siah, K. W., & Lo, A. W. (2019). Estimation of clinical trial success rates and related parameters. Biostatistics, 20(2), 273–286. https://doi.org/10.1093/biostatistics/kxx069 
  39. Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z 
  40. Baek, M., DiMaio, F., Anishchenko, I., et al. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373(6557), 871–876. https://doi.org/10.1126/science.abj8754
  41. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority oversampling technique. Journal of Artificial Intelligence Research, 16, 321–357.  https://doi.org/10.1613/jair.953
  42. Hey, T., Tansley, S., & Tolle, K. (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research.
  43. Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: Present status and future prospects. Drug Discovery Today, 24(3), 773–780. https://doi.org/10.1016/j.drudis.2018.11.014
  44. Szklarczyk, D., Gable, A. L., Lyon, D., et al. (2021). STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide datasets. Nucleic Acids Research, 47(D1), D607–D613. https://doi.org/10.1093/nar/gky1131
  45. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
  46. Vamathevan, J., Clark, D., Czodrowski, P., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477. https://doi.org/10.1038/s41573-019-0024-5
  47. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587019-0224-x.

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Sayali Pagire
Corresponding author

Pharmaceutics, SND college of pharmacy, Babhulgaon

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Aadesh Varpe
Co-author

Pharmaceutics, SND college of pharmacy, Babhulgaon

Photo
Om Ugalmugale
Co-author

Pharmaceutics, SND college of pharmacy, Babhulgaon

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Aditya Vighne
Co-author

Pharmaceutics, SND college of pharmacy, Babhulgaon

Aadesh Varpe, Om Ugalmugale, Sayali Pagire*, Aditya Vighne, Artificial Intelligence in Drug Discovery, Int. J. Sci. R. Tech., 2025, 2 (10), 435-444. https://doi.org/10.5281/zenodo.17444647

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