Authors : Saranya Balaguru; Alekhya Gandra

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/ymt6bwem

Scribd : https://tinyurl.com/y84pamc3

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV958

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Advancements in generative artificial intelligence (AI) are reshaping the drug discovery landscape by introducing automated, data-driven workflows that significantly reduce development time and cost. This paper explores a process discovery and automation workflow tailored to generative AI applications in drug discovery, covering the key stages from data ingestion and preprocessing to molecule generation, validation, and optimization [1]. Through the lens of process discovery, we identify critical bottlenecks and opportunities for automation within traditional drug discovery workflows, demonstrating how generative AI, particularly models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can efficiently generate diverse molecular candidates. Each stage of the workflow integrates automation to streamline high-throughput virtual screening, optimize lead compounds, and enhance predictive accuracy for pharmacological properties such as bioavailability, efficacy, and safety. By embedding automation into these processes, generative AI accelerates not only the generation of candidate compounds but also their assessment against complex biological criteria. The paper further addresses challenges in data quality, interpretability, and regulatory compliance while showcasing real-world case studies where AI-driven process automation led to breakthrough therapeutic discoveries. This structured workflow offers a blueprint for researchers and industry professionals seeking to leverage process automation and generative AI to drive innovation, efficiency, and scalability in drug discovery [1].

Keywords : Generative AI, Drug Discovery, Process improvement, Healthcare, Automation.

References :

1. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017). Drug discovery with generative adversarial networks for de novo molecular design. Molecular Pharmaceutics, 14(9), 3098–3104. doi:10.1021/acs.molpharmaceut.7b0034
2. Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4(2), 268–276. doi:10.1021/acscentsci.7b00572
3. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, A. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., Volkov, Y., Zholus, A., & Mamoshina, P. (2020). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. doi:10.1038/s41587-019-0224-x
4. Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688–702.e13. doi:10.1016/j.cell.2020.01.021
5. Merk, D., Friedrich, L., Grisoni, F., & Schneider, G. (2018). De novo design of bioactive small molecules by artificial intelligence. Molecular Informatics, 37(1–2), 1700153. doi:10.1002/minf.201700153
6. Molnar, C. (2019). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Leanpub.
7. Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017). Molecular de novo design through deep reinforcement learning. Journal of Cheminformatics, 9(1), 48. doi:10.1186/s13321-017-0235-x
8. McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. C., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., … Suleyman, M. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94. doi:10.1038/s41586-019-1799-6
9. De Cao, N., & Kipf, T. (2018). MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973.
10. Wang, X., Zhang, Y., Jiang, T., & Wang, H. (2021). Transformers in medicinal chemistry and drug discovery: A comprehensive review. Journal of Medicinal Chemistry, 64(16), 11780–11797. doi:10.1021/acs.jmedchem.1c00427
11. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Yau, C., & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477. doi:10.1038/s41573-019-0024-5
12. Walters, W. P., Murcko, M. A., & Feher, M. (1999). Recognizing molecules with drug-like properties. Current Opinion in Chemical Biology, 3(4), 384–387. doi:10.1016/S1367-5931(99)80058-9
13. Schneider, G. (2018). Automating drug discovery. Nature Reviews Drug Discovery, 17(2), 97–113. doi:10.1038/nrd.2017.232
14. Segler, M. H., Preuss, M., & Waller, M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698), 604–610. doi:10.1038/nature25978
15. Mouchlis, V. D., Afantitis, A., Serra, A., & Melagraki, G. (2021). Advancing computational drug discovery with artificial intelligence. Expert Opinion on Drug Discovery, 16(4), 457–469. doi:10.1080/17460441.2021.1859370
16. Polishchuk, P. G., Madzhidov, T. I., & Varnek, A. (2013). Estimation of the size of drug-like chemical space based on GDB-17 data. Journal of Computer-Aided Molecular Design, 27(8), 675–679. doi:10.1007/s10822-013-9672-4
17. Alaimo, S., Pulvirenti, A., & Ferro, A. (2013). Drug-target interaction prediction through domain-tuned network-based inference. Bioinformatics, 29(16), 2004–2008. doi:10.1093/bioinformatics/btt305
18. 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. doi:10.1016/j.drudis.2018.01.039
19. Sun, W., Sanderson, P. E., & Zheng, W. (2016). Drug combination therapy increases successful drug repositioning. Drug Discovery Today, 21(7), 1189–1195. doi:10.1016/j.drudis.2016.05.015
20. Lavecchia, A. (2015). Machine-learning approaches in drug discovery: Methods and applications. Drug Discovery Today, 20(3), 318–331. doi:10.1016/j.drudis.2014.10.012