Chapter 1. Artificial neural networks in pharmaceutical research

Authors

Yaroslava Pushkarova
Bogomolets National Medical University
https://orcid.org/0000-0001-9856-7846
Galina Zaitseva
Bogomolets National Medical University
https://orcid.org/0000-0003-3138-6324
Keywords: artificial intelligence, pharmaceutical research, drug discovery, drug design, machine learning, pharmacokinetics, personalized medicine, data analysis

Synopsis

In press. Chemical engineering: innovations in materials and processes

Artificial neural networks (ANNs) are revolutionizing pharmaceutical research, offering innovative solutions to complex challenges such as drug discovery, prediction of pharmacokinetic properties, toxicity analysis, drug formulation optimization, and personalized medicine. By mimicking the way, the human brain processes information, ANNs enable to analyze vast and intricate datasets, making them particularly valuable in the pharmaceutical industry, where data is often multidimensional and complex. The integration of ANNs has the potential to accelerate the drug development process, reduce costs, and improve the safety and efficacy of therapeutic interventions. The continued advancement of computational power, the availability of large-scale biomedical data, and the refinement of machine learning techniques have positioned ANNs as indispensable tools for predicting drug behavior, identifying potential risks, and optimizing the drug development pipeline. Furthermore, ANNs enable researchers to uncover hidden patterns within data, offering insights that might otherwise remain inaccessible through traditional methods.

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Author Biographies

Yaroslava Pushkarova, Bogomolets National Medical University

PhD, Associate Professor
Department of Analytical, Physical and Colloid Chemistry
https://orcid.org/0000-0001-9856-7846

Corresponding author
yaroslava.pushkarova@gmail.com

Galina Zaitseva, Bogomolets National Medical University

PhD, Associate Professor, Head of Department
Department of Analytical, Physical and Colloid Chemistry
https://orcid.org/0000-0003-3138-6324

References

Mak, K.-K., Wong, Y.-H., Pichika, M. R. (2024). Artificial Intelligence in Drug Discovery and Development. Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays. Cham: Springer, 1461–1498. https://doi.org/10.1007/978-3-031-35529-5_92

Lu, M., Yin, J., Zhu, Q., Lin, G., Mou, M., Liu, F. et al. (2023). Artificial Intelligence in Pharmaceutical Sciences. Engineering, 27, 37–69. https://doi.org/10.1016/j.eng.2023.01.014

Pushkarova, Y., Kholin, Y. (2014). A procedure for meaningful unsupervised clustering and its application for solvent classification. Open Chemistry, 12 (5), 594–603. https://doi.org/10.2478/s11532-014-0514-6

Wu, Y., Feng, J. (2018). Development and Application of Artificial Neural Network. Wireless Personal Communications, 102 (2), 1645–1656. https://doi.org/10.1007/s11277-017-5224-x

Mishra, C., Gupta, D. L. (2017). Deep Machine Learning and Neural Networks: An Overview. IAES International Journal of Artificial Intelligence (IJ-AI), 6 (2), 66. https://doi.org/10.11591/ijai.v6.i2.pp66-73

Grossi, E., Buscema, M. (2007). Introduction to artificial neural networks. European Journal of Gastroenterology & Hepatology, 19 (12), 1046–1054. https://doi.org/10.1097/meg.0b013e3282f198a0

Zou, J., Han, Y., So, S.-S. (2008). Overview of Artificial Neural Networks. Artificial Neural Networks. Humana Press, 14–22. https://doi.org/10.1007/978-1-60327-101-1_2

Yanling, Z., Bimin, D., Zhanrong, W. (2002). Analysis and study of perceptron to solve XOR problem. The 2nd International Workshop on Autonomous Decentralized System. Beijing: IEEE, 168–173. https://doi.org/10.1109/iwads.2002.1194667

Anwar, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M. K. (2018). Medical Image Analysis using Convolutional Neural Networks: A Review. Journal of Medical Systems, 42 (11). https://doi.org/10.1007/s10916-018-1088-1

Sarvamangala, D. R., Kulkarni, R. V. (2022). Convolutional neural networks in medical image understanding: a survey. Evolutionary Intelligence, 15 (1), 1–22. https://doi.org/10.1007/s12065-020-00540-3

Carpenter, K. A., Cohen, D. S., Jarrell, J. T., Huang, X. (2018). Deep Learning and Virtual Drug Screening. Future Medicinal Chemistry, 10 (21), 2557–2567. https://doi.org/10.4155/fmc-2018-031

Kimber, T. B., Chen, Y., Volkamer, A. (2021). Deep Learning in Virtual Screening: Recent Applications and Developments. International Journal of Molecular Sciences, 22 (9), 4435. https://doi.org/10.3390/ijms22094435

Moon, S., Zhung, W., Yang, S., Lim, J., Kim, W. Y. (2022). PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions. Chemical Science, 13 (13), 3661–3673. https://doi.org/10.1039/d1sc06946b

Suresh, N., Chinnakonda Ashok Kumar, N., Subramanian, S., Srinivasa, G. (2022). Memory augmented recurrent neural networks for de-novo drug design. PLOS ONE, 17 (6), e0269461. https://doi.org/10.1371/journal.pone.0269461

Ruiz Puentes, P., Valderrama, N., González, C., Daza, L., Muñoz-Camargo, C., Cruz, J. C., Arbeláez, P. (2021). PharmaNet: Pharmaceutical discovery with deep recurrent neural networks. PLOS ONE, 16 (4), e0241728. https://doi.org/10.1371/journal.pone.0241728

Kaliuzhenko, A., Pushkarova, Y. (2023). Application of artificial neural networks for solving pharmaceutical issues. Grail of Science, 24, 766–769. https://doi.org/10.36074/grail-of-science.17.02.2023.143

Wishart, D. S. (2007). Improving Early Drug Discovery through ADME Modelling. Drugs in R & D, 8 (6), 349–362. https://doi.org/10.2165/00126839-200708060-00003

Lucas, A. J., Sproston, J. L., Barton, P., Riley, R. J. (2019). Estimating human ADME properties, pharmacokinetic parameters and likely clinical dose in drug discovery. Expert Opinion on Drug Discovery, 14 (12), 1313–1327. https://doi.org/10.1080/17460441.2019.1660642

Siramshetty, V. B., Xu, X., Shah, P. (2023). Artificial Intelligence in ADME Property Prediction. Computational Drug Discovery and Design. New York: Springer, 307–327. https://doi.org/10.1007/978-1-0716-3441-7_17

Wieder, O., Kuenemann, M., Wieder, M., Seidel, T., Meyer, C., Bryant, S. D., Langer, T. (2021). Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks. Molecules, 26 (20), 6185. https://doi.org/10.3390/molecules26206185

Tosca, E. M., Bartolucci, R., Magni, P. (2021). Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules. Pharmaceutics, 13 (7), 1101. https://doi.org/10.3390/pharmaceutics13071101

Pushkarova, Y., Panchenko, V., Kholin, Y. (2021). Application an Artificial Neural Network for Prediction of Substances Solubility. IEEE EUROCON 2021 – 19th International Conference on Smart Technologies. Lviv: IEEE, 82–87. https://doi.org/10.1109/eurocon52738.2021.9535593

Li, G., Zhang, N., Dai, X., Fan, L. (2024). EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity. Journal of Chemical Information and Modeling, 64 (15), 5912–5921. https://doi.org/10.1021/acs.jcim.4c00920

Tolle, K. M., Chen, H., Chow, H.-H. (2000). Estimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data sets. Decision Support Systems, 30 (2), 139–151. https://doi.org/10.1016/s0167-9236(00)00094-4

Turner, J. V., Maddalena, D. J., Cutler, D. J. (2004). Pharmacokinetic parameter prediction from drug structure using artificial neural networks. International Journal of Pharmaceutics, 270 (1-2), 209–219. https://doi.org/10.1016/j.ijpharm.2003.10.011

Sutariya, V., Groshev, A., Sadana, P., Bhatia, D., Pathak, Y. (2013). Artificial Neural Network in Drug Delivery and Pharmaceutical Research. The Open Bioinformatics Journal, 7 (1), 49–62. https://doi.org/10.2174/1875036201307010049

Guengerich, F. P. (2011). Mechanisms of Drug Toxicity and Relevance to Pharmaceutical Development. Drug Metabolism and Pharmacokinetics, 26 (1), 3–14. https://doi.org/10.2133/dmpk.dmpk-10-rv-062

Pushkarova, Y., Zaitseva, G., Saker, M. A. (2022). Prediction of Toxicity of Phenols Using Artificial Neural Networks. 2022 12th International Conference on Advanced Computer Information Technologies (ACIT). Ruzomberok: IEEE, 493–496. https://doi.org/10.1109/acit54803.2022.9913174

Pushkarova, Y., Zaitseva, G., Kaliuzhenko, A. (2023). Classification of Residual Solvents by Risk Assessment Using Chemometric Methods. 2023 13th International Conference on Advanced Computer Information Technologies (ACIT). Wrocław: IEEE, 562–565. https://doi.org/10.1109/acit58437.2023.10275405

Hemmerich, J., Ecker, G. F. (2020). In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways. WIREs Computational Molecular Science, 10 (4). https://doi.org/10.1002/wcms.1475

Cavasotto, C. N., Scardino, V. (2022). Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point. ACS Omega, 7 (51), 47536–47546. https://doi.org/10.1021/acsomega.2c05693

Canzler, S., Schor, J., Busch, W., Schubert, K., Rolle-Kampczyk, U. E., Seitz, H. et al. (2020). Prospects and challenges of multi-omics data integration in toxicology. Archives of Toxicology, 94 (2), 371–388. https://doi.org/10.1007/s00204-020-02656-y

Mukherjee, A., Abraham, S., Singh, A., Balaji, S., Mukunthan, K. S. (2024). From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Molecular Biotechnology, 67 (4), 1269–1289. https://doi.org/10.1007/s12033-024-01133-6

Li, P., Zhao, L. (2007). Developing early formulations: Practice and perspective. International Journal of Pharmaceutics, 341 (1-2), 1–19. https://doi.org/10.1016/j.ijpharm.2007.05.049

Byron, P. R. (2004). Drug Delivery Devices: Issues in Drug Development. Proceedings of the American Thoracic Society, 1 (4), 321–328. https://doi.org/10.1513/pats.200403-023ms

Takayama, K., Fujikawa, M., Nagai, T. (1999). Artificial Neural Network as a Novel Method to Optimize Pharmaceutical Formulations. Pharmaceutical Research, 16 (1), 1–6. https://doi.org/10.1023/a:1011986823850

Wang, N., Sun, H., Dong, J., Ouyang, D. (2021). PharmDE: A new expert system for drug-excipient compatibility evaluation. International Journal of Pharmaceutics, 607, 120962. https://doi.org/10.1016/j.ijpharm.2021.120962

Soliman, M. E., Adewumi, A. T., Akawa, O. B., Subair, T. I., Okunlola, F. O., Akinsuku, O. E., Khan, S. (2022). Simulation Models for Prediction of Bioavailability of Medicinal Drugs – the Interface Between Experiment and Computation. AAPS PharmSciTech, 23 (3). https://doi.org/10.1208/s12249-022-02229-5

Arav, Y. (2024). Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically Based Pharmacokinetic, and First-Principal Models. https://doi.org/10.20944/preprints202406.0471.v1

Ibrić, S., Jovanović, M., Djurić, Z., Parojčić, J., Solomun, L., Lučić, B. (2007). Generalized regression neural networks in prediction of drug stability. Journal of Pharmacy and Pharmacology, 59 (5), 745–750. https://doi.org/10.1211/jpp.59.5.0017

Wang, S., Di, J., Wang, D., Dai, X., Hua, Y., Gao, X., Zheng, A., Gao, J. (2022). State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation. Pharmaceutics, 14 (1), 183. https://doi.org/10.3390/pharmaceutics14010183

Rafienia, M., Amiri, M., Janmaleki, M., Sadeghian, A. (2010). Application of artificial neural networks in controlled drug delivery systems. Applied Artificial Intelligence, 24 (8), 807–820. https://doi.org/10.1080/08839514.2010.508606

Sun, Y., Peng, Y., Chen, Y., Shukla, A. J. (2003). Application of artificial neural networks in the design of controlled release drug delivery systems. Advanced Drug Delivery Reviews, 55 (9), 1201–1215. https://doi.org/10.1016/s0169-409x(03)00119-4

Syrowatka, A., Song, W., Amato, M. G., Foer, D., Edrees, H., Co, Z. et al. (2022). Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. The Lancet Digital Health, 4 (2), e137–e148. https://doi.org/10.1016/s2589-7500(21)00229-6

Lee, C. Y., Chen, Y. P. (2021). Descriptive prediction of drug side‐effects using a hybrid deep learning model. International Journal of Intelligent Systems, 36 (6), 2491–2510. https://doi.org/10.1002/int.22389

Schork, N. J. (2019). Artificial Intelligence and Personalized Medicine. Precision Medicine in Cancer Therapy. Cham: Springer, 265–283. https://doi.org/10.1007/978-3-030-16391-4_11

Bonkhoff, A. K., Grefkes, C. (2021). Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain, 145 (2), 457–475. https://doi.org/10.1093/brain/awab439

Kang, S. (2018). Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks. Artificial Intelligence in Medicine, 85, 1–6. https://doi.org/10.1016/j.artmed.2018.02.004

Wanichthanarak, K., Fahrmann, J. F., Grapov, D. (2015). Genomic, Proteomic, and Metabolomic Data Integration Strategies. Biomarker Insights, 10s4, BMI.S29511. https://doi.org/10.4137/bmi.s29511

Valous, N. A., Popp, F., Zörnig, I., Jäger, D., Charoentong, P. (2024). Graph machine learning for integrated multi-omics analysis. British Journal of Cancer, 131 (2), 205–211. https://doi.org/10.1038/s41416-024-02706-7

Alharbi, W. S., Rashid, M. (2022). A review of deep learning applications in human genomics using next-generation sequencing data. Human Genomics, 16 (1). https://doi.org/10.1186/s40246-022-00396-x

van IJzendoorn, D. G. P., Szuhai, K., Briaire-de Bruijn, I. H., Kostine, M., Kuijjer, M. L., Bovée, J. V. M. G. (2019). Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas. PLOS Computational Biology, 15 (2), e1006826. https://doi.org/10.1371/journal.pcbi.1006826

Zhang, P., Zhang, W., Sun, W., Xu, J., Hu, H., Wang, L., Wong, L. (2024). Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network. BMC Genomics, 25 (1). https://doi.org/10.1186/s12864-024-09967-9

Sundaram, L., Gao, H., Padigepati, S. R., McRae, J. F., Li, Y., Kosmicki, J. A. et al. (2018). Predicting the clinical impact of human mutation with deep neural networks. Nature Genetics, 50 (8), 1161–1170. https://doi.org/10.1038/s41588-018-0167-z

Kellerman, R., Nayshool, O., Barel, O., Paz, S., Amariglio, N., Klang, E., Rechavi, G. (2024). Mutation Pathogenicity Prediction by a Biology Based Explainable AI Multi-Modal Algorithm. https://doi.org/10.1101/2024.06.05.24308476

van Hilten, A., Kushner, S. A., Kayser, M., Ikram, M. A., Adams, H. H. H., Klaver, C. C. W. et al. (2021). GenNet framework: interpretable deep learning for predicting phenotypes from genetic data. Communications Biology, 4 (1). https://doi.org/10.1038/s42003-021-02622-z

Wen, B., Zeng, W., Liao, Y., Shi, Z., Savage, S. R., Jiang, W., Zhang, B. (2020). Deep Learning in Proteomics. Proteomics, 20 (21-22). https://doi.org/10.1002/pmic.201900335

Holley, L. H., Karplus, M. (1989). Protein secondary structure prediction with a neural network. Proceedings of the National Academy of Sciences, 86 (1), 152–156. https://doi.org/10.1073/pnas.86.1.152

Pearce, R., Zhang, Y. (2021). Deep learning techniques have significantly impacted protein structure prediction and protein design. Current Opinion in Structural Biology, 68, 194–207. https://doi.org/10.1016/j.sbi.2021.01.007

Jha, K., Saha, S., Singh, H. (2022). Prediction of protein–protein interaction using graph neural networks. Scientific Reports, 12 (1). https://doi.org/10.1038/s41598-022-12201-9

Li, X., Han, P., Wang, G., Chen, W., Wang, S., Song, T. (2022). SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction. BMC Genomics, 23 (1). https://doi.org/10.1186/s12864-022-08687-2

Huang, Y.-X., Liu, R. (2024). Improved prediction of post-translational modification crosstalk within proteins using DeepPCT. Bioinformatics, 40 (12). https://doi.org/10.1093/bioinformatics/btae675

Gutierrez, C. S., Kassim, A. A., Gutierrez, B. D., Raines, R. T. (2024). Sitetack: A Deep Learning Model that Improves PTM Prediction by Using Known PTMs, 40 (11). https://doi.org/10.1101/2024.06.03.596298

Chiu, Y.-C., Chen, H.-I. H., Zhang, T., Zhang, S., Gorthi, A., Wang, L.-J. et al. (2019). Predicting drug response of tumors from integrated genomic profiles by deep neural networks. BMC Medical Genomics, 12 (S1). https://doi.org/10.1186/s12920-018-0460-9

Smelik, M., Zhao, Y., Li, X., Loscalzo, J., Sysoev, O., Mahmud, F. et al. (2024). An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases. Scientific Reports, 14 (1). https://doi.org/10.1038/s41598-024-63399-9

Nagy, B., Galata, D. L., Farkas, A., Nagy, Z. K. (2022). Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing – a Review. The AAPS Journal, 24 (4). https://doi.org/10.1208/s12248-022-00706-0

Saha, G. C., Eni, L. N., Saha, H., Parida, P. K., Rathinavelu, R., Jain, S. K., Haldar, B. (2023). Artificial Intelligence in Pharmaceutical Manufacturing: Enhancing Quality Control and Decision Making. Rivista Italiana di Filosofia Analitica Junior, 14 (2), 116–126.

Raisul Islam, M., Zakir Hossain Zamil, M., Eshmam Rayed, M., Mohsin Kabir, M., Mridha, M. F. et al. (2024). Deep Learning and Computer Vision Techniques for Enhanced Quality Control in Manufacturing Processes. IEEE Access, 12, 121449–121479. https://doi.org/10.1109/access.2024.3453664

Zope, K., Singh, K., Nistala, S. H., Basak, A., Rathore, P., Runkana, V. (2019). Anomaly Detection and Diagnosis In Manufacturing Systems: A comparative study of statistical, machine learning and deep learning techniques. Annual Conference of the PHM Society, 11 (1). https://doi.org/10.36001/phmconf.2019.v11i1.815

Motwani, K., Dsouza, R., Dsouza, R., Jose, J. (2022). Counterfeit medicine detection using deep learning. International Journal of Innovative Research in Technology, 9 (3), 118–821.

Alsallal, M., Sharif, M. S., Al-Ghzawi, B., Mlkat al Mutoki, S. M. (2018). A Machine Learning Technique to Detect Counterfeit Medicine Based on X-Ray Fluorescence Analyser. 2018 International Conference on Computing, Electronics & Communications Engineering (ICCECE). Southend: IEEE, 118–122. https://doi.org/10.1109/iccecome.2018.8659110

Liu, X., Meehan, J., Tong, W., Wu, L., Xu, X., Xu, J. (2020). DLI-IT: a deep learning approach to drug label identification through image and text embedding. BMC Medical Informatics and Decision Making, 20 (1). https://doi.org/10.1186/s12911-020-1078-3

Rimdusit, P., Aphiromyarnont, P., Phimoltares, S., Panthuwadeethorn, S. (2024). Extracting Information from Drug Label Using Image and Text Processing. 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE). Phuket: IEEE, 377–382. https://doi.org/10.1109/jcsse61278.2024.10613666

Choi, S. H., Poon, C. H. (2008). An RFID-based anti-counterfeiting system. IAENG International Journal of Computer Science, 35 (1).

Rahman, F., Ahamed, S. I. (2012). Efficient detection of counterfeit products in large-scale RFID systems using batch authentication protocols. Personal and Ubiquitous Computing, 18 (1), 177–188. https://doi.org/10.1007/s00779-012-0629-8

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