ARTIFICIAL INTELLIGENCE IN PHYTOCHEMICAL RESEARCH: MAPPING THE PLANT KINGDOM FOR DRUG DISCOVER
Main Article Content
Keywords
Artificial intelligence, machine learning, botanical therapeutics, phytochemical research, natural product discovery, deep learning, computer vision, omics integration, plant species identification, bioactive compounds, ethnobotany, drug discovery, metabolomics, proteomics, genomics, AI interpretability, interdisciplinary collaboration
Abstract
The vast diversity of the plant kingdom represents both an incredible reservoir of potential therapeutic agents and a formidable challenge for drug discovery. Traditional approaches to identifying medicinally valuable plants are labor-intensive, time-consuming, and often limited by narrow ethnobotanical scope. In recent years, the integration of artificial intelligence (AI) and machine learning (ML) has revolutionized this process, offering new methods to analyze and prioritize botanical species with pharmacological potential more efficiently and systematically.
This review explores the transformative role of AI and ML in modern phytochemical research and natural product discovery. It highlights how these technologies are enabling more precise, data-driven selection of plant species by leveraging diverse datasets—including ethnobotanical records, phytochemical compositions, and bioactivity profiles. AI-powered predictive models can identify patterns and correlations across complex datasets that may not be apparent through conventional statistical methods. This leads to the early detection of bioactive compounds and novel therapeutic leads.
Advancements in deep learning and computer vision have also enhanced image-based plant identification and disease diagnostics, improving taxonomic accuracy and aiding in species classification, especially in under-documented regions. Moreover, the integration of omics data—genomics, transcriptomics, proteomics, and metabolomics—with AI models is facilitating a systems-level understanding of plant metabolic pathways and their therapeutic outputs.
Despite its promise, AI-driven botanical research faces challenges, including data quality, model interpretability, and the need for interdisciplinary collaboration. Nonetheless, the potential for accelerating natural product discovery through AI is profound. By automating and optimizing the exploration of plant biodiversity, AI stands to unlock new frontiers in drug discovery, conservation biology, and personalized medicine.
This review presents a comprehensive overview of the current landscape, identifies key breakthroughs and limitations, and outlines future directions for AI-assisted exploration of the botanical seascape.
References
2. Alizadehsani, R., Oyelere, S. S., Hussain, S., Calixto, R. R., Albuquerque, V. H. C., Roshanzamir, M., Rahouti, M., & Jagatheesaperumal, S. (2023). Explainable artificial intelligence for drug discovery and development: a comprehensive survey.
3. Axios. (2025). Digital twins in agriculture: AI modeling of apple trees at Michigan State University. Retrieved June 26, 2025.
4. Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
5. Frontiers in Plant Science. (2023). AI applications in plant disease diagnostics and phytochemical research. Frontiers in Plant Science. Gurulingappa, H., Rajesh Bhat, K., & Ravindran, M. (2022). Machine learning enhances prediction of plants as potential sources of antimalarials. BMC Complementary Medicine and Therapies.
6. Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, Article 1419.
7. Pullagura, J. R., Rao, A. N., Kumar, P. S., Raju, M. V., & Madhavi, B. (2025). Grain Quality Analysis Using CNN and Iot Based Strategic Safeguarding. Metallurgical and Materials Engineering, 1273-1278.
8. Sai, R. H. N., Madhavi, B., Reginald, P. J., Kumar, P. S., & Raju, M. V. (2025). Tracing The Pathways and Health Risks of Microplastics in The Food Chain. Metallurgical and Materials Engineering, 1448-1454.
9. Syam Babu, D., Vijay, K., Shakira, S., Mallemkondu, V. S., Barik, P., Kuppam, C., ... & Raju, M. V. (2025). Laterite integrated persulfate based advanced oxidation and biological treatment for textile industrial effluent remediation: optimization and field application. Applied Bionics and Biomechanics, 2025(1), 9325665.
10. MV, R., & K Maria, D. (2020). Green Engineering Strategies on Solid Waste Management to Promote Urban Environmental Sustainability. International Journal of Advanced Science and Technology, 29(5), 4638-4648.
11. Raju, M. V., Palivela, H., Mariadas, K., & Babu, S. R. (2019). Assessment of physico-chemical and biological characteristics and suitability study of lake water: a model study. Technology, 10(01), 1431-1438.
12. MV, R., K Maria, D., & Cyril Lucy, M. (2020). Rapid Environmental Impact Assessment of Lake Water. International Journal of Advanced Science and Technology, 29(3), 8468-8478.
13. Raju, M. V., Mariadas, K., Palivela, H., Ramesh Babu, S., & Raja Krishna Prasad, N. (2018). Mitigation plans to overcome environmental issues: A model study. International Journal of Civil Engineering and Technology, 9(10), 86-94.
14. Satish Kumar, M., Raju, M. V., & Palivela, H. (2017). Comprehensive index of groundwater prospects by using standard protocols-A model study. International Journal of Civil Engineering and Technology (IJCIET), 8(5), 521-526.
15. Hepsibah, P., MV, R., & K Maria, D. (2020). Quality Characteristics of Ground Waters in Few Sources of Industrial Zone. International Journal of Innovative Technology and Exploring Engineering, 9(3), 1134-1137.
16. Kumar, M. S., Raju, M. V., Palivela, H., & Venu Ratna Kumari, G. (2017). Water quality scenario of urban polluted lakes-A model study. International Journal of Civil Engineering and Technology, 8(5), 297-302.
17. Kumar, M. S., Raju, M. V., & Palivela, H. (2017). An overview of managing municipal Solid waste in urban areas-A model study. International Journal of Civil Engineering and Technology, 8(5).
18. Satish Kumar, M., Raju, M. V., Ramesh Babu, S., & Siva Jagadeesh Kumar, M. (2017). Interpretation and correlative study of water simulation in surface water bodies. International Journal of Civil Engineering and Technology (IJCIET), 8(5), 1206-1211.
19. Pullagura, J. R., Osman, O., Kumar, M. S., & Raju, M. V. (2024). LoRaWAN based IoT Architecture for Environmental Monitoring in Museums. Journal of Computational Analysis & Applications, 33(5).
20. Mariadas, K., Rao, T. S., Raju, M. V., & Kumar, M. S. (2024). A Study On the Fluoride Content of the Groundwater in The Gurazala Division of the Palnadu District, Andhra Pradesh, India. Journal of Advanced Zoology, 45(3).
21. Geetha, R., Raju, M. V., Bhavanee, G. P., & Selvakumar, P. (2022). A Review on Medicinal Activity and Its Health Impact of Azadirachta Indica (Neem). YMER., vol. 21(12), 188-200.
22. Reginald, P. J., Raju, M. V., Satyanarayana, D., Mariadas, K., & Kumar, M. S. (2021). Kisaan Seva-Aadhaar Linked Smart Farming Application. Annals of the Romanian Society for Cell Biology, 25(5), 5168-5176.
23. Richard Bollans, A., Aitken, C., Antonelli, A., Bitencourt, C., Goyder, D., Lucas, E., Ondo, I., Pérez Escobar, O. A., Pironon, S., Richardson, J. E., Russell, D., Silvestro, D., Wright, C. W., & Howes, M. J. R. (2023). Machine learning enhances prediction of plants as potential sources of antimalarials. Frontiers in Plant Science, 14, Article 1173328.
24. Richard-Bollans, L., et al. (2023). Machine learning classifiers predict antiplasmodial potential in Apocynaceae, Loganiaceae, and Rubiaceae with higher precision than ethnobotanical methods. Frontiers in Pharmacology.
25. Varghese, J., et al. (2025). Multi-omics integration and AI for metabolite discovery.
26. Varghese, R., Shringi, H., Ramamoorthy, S., & Efferth, T. (2025). Artificial intelligence driven approaches in phytochemical research: trends and prospects. Photochemistry Reviews.
27. Vutukuru, S. S., Asadi, S. S., Vasantha, R. B. V. T., & Raju, M. V. (2012). Plankton biodiversity as indicators of the ecological status of River Moosi, Hyderabad, India. International Journal of Earth Science and Engineering, 5(3), 587-592.
28. Wikipedia contributors. (2024). Plant Net. In Wikipedia, The Free Encyclopedia. Retrieved June 26, 2025.
29. Wikipedia contributors. (2025). Evogene and Insilico Medicine. In Wikipedia, The Free Encyclopedia.
30. Wikipedia contributors. (n.d.). Artificial intelligence in pharmacy. In Wikipedia. Retrieved June 2025.
31. Wikipedia contributors. (n.d.). Plant Net. In Wikipedia. Retrieved June 2025.
32. Zhao, L., Walkowiak, S., & Fernando, W. G. D. (2023). Artificial intelligence: a promising tool in exploring the phytomicrobiome in managing disease and promoting plant health. Plants, 12(9), Article 1852.
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Harikrishna Ramaprasad Saripalli
Technical Director, King Honey Limited, Owned and Operated by Me|Today Limited, 1/6 Mahoe Street, Tauhara, RD2, Taupo 3378, New Zealand
Rajasekhar Dega
Lecturer in Botany, K.R.K Government Degree College, Addanki, Andhra Pradesh, India.
Uma Devarapalli
Lecturer in Botany, Government College for Women (Autonomous), Guntur, Andhra Pradesh 522002, India.
Nitchal Kiran Jaladi
Professor, Department of Physics, Vignan’s Foundation for Science, Technology and Research, Deemed to be University, Guntur, Andhra Pradesh, 522213, India.
P.V. Hemalatha
Senior Lecturer in Chemistry, K.R.K Government Degree College, Addanki, Andhra Pradesh, India.
Jala Aaron Hemanth Reuveng
B.Tech. Student, Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, Guntur, 522502, India.