About the Journal
Editorial Board
Editorial Office and Publisher
Ownership
Article Processing Charge
Open Access and Licensing
Personal Data Protection
Terms and Conditions: Cookie policy and Privacy policy
Copyright and Copyright Transfer Agreement
Visagaa Publishing House Repository Policy
Visagaa Publishing House CrossMark Policy
The Initiative for Open Citations (I4OC)
Archiving
Plagiarism Policy
Online first
Current issue
Archive
Ethics Statements for Natural Resources for Human Health
Ethical Guidelines
Post-Publication Policies
Responsibilities of Editors and Publishers
Conflict of Interest Statements for Natural Resources for Human Health
Human and Animal Rights Policy
Complaints and Appeals Policy
Allegations of Research Misconduct Policy
Authorship and Contributorship Policy
Publication Ethics and Malpractice Policy
Ethics approval and informed consent
Guidelines for the Use of Generative AI and AI-Assisted Technologies in Manuscripts for Natural Resources for Human Health
For authors
Instructions for Authors
Submitting manuscript – step by step
Authorship and Contributor Guidelines
For reviewers
The peer-review process
Recognition for Reviewers
Visagaa Author Services
About the Journal
Editorial Board
Editorial Office and Publisher
Ownership
Article Processing Charge
Open Access and Licensing
Personal Data Protection
Terms and Conditions: Cookie policy and Privacy policy
Copyright and Copyright Transfer Agreement
Visagaa Publishing House Repository Policy
Visagaa Publishing House CrossMark Policy
The Initiative for Open Citations (I4OC)
Archiving
Plagiarism Policy
Ethics Statements for Natural Resources for Human Health
Ethical Guidelines
Post-Publication Policies
Responsibilities of Editors and Publishers
Conflict of Interest Statements for Natural Resources for Human Health
Human and Animal Rights Policy
Complaints and Appeals Policy
Allegations of Research Misconduct Policy
Authorship and Contributorship Policy
Publication Ethics and Malpractice Policy
Ethics approval and informed consent
Guidelines for the Use of Generative AI and AI-Assisted Technologies in Manuscripts for Natural Resources for Human Health
| eISSN: | 2583-1194 |
REVIEW PAPER
Integrating Artificial Intelligence and Machine Learning in Natural Product Discovery: From Omics Data to Drug Design
1
Department of Microbiology, Auxilium College (Autonomous), Vellore, India
2
Department of Microbiology, Auxilium College (Autonomous), Vellore – 6, India
3
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India
4
Chitkara college of Pharmacy, Centre for Research Impact & Outcome, Chitkara College of Pharmacy, Chitkara University, Rajpura, 140401, Punjab, India, India
Submission date: 2025-10-29
Final revision date: 2025-11-21
Acceptance date: 2026-01-25
Online publication date: 2026-06-30
Corresponding author
Hitesh Chopra
Chitkara College of Pharmacy, Center for Research Impact & Outcome, Chitkara University, Rajpura, Punjab, India
Chitkara College of Pharmacy, Center for Research Impact & Outcome, Chitkara University, Rajpura, Punjab, India
KEYWORDS
TOPICS
ABSTRACT
Natural products represent an essential source of bioactive materials with potent biological activity, traditional approaches for their discovery and development remain time consuming and resource-intensive. Recent advances of Artificial Intelligence (AI) and machine learning (ML) are transforming natural product research by enabling predictive modeling, high throughput screening, automated compound identification and integration of complex omics datasets. Other AI and ML methods such as deep learning, neural networks and generative algorithms are enhancing drug discovery by predicting bioactivity and structural elucidation, and optimizing pharmacokinetic properties of natural products. Furthermore, AI, ML, and other technologies are opening new possibilities, such as genome mining for biosynthetic gene clusters, promoting dereplication strategies, and integrating multi-omics datasets to understand natural product biosynthesis and pharmacology at the systems level. This review summarizes the recent applications of AI and ML in natural product discovery, metabolomics, cheminformatics and pharmacology, present highlighted successful case studies, associated challenges and future perspectives for AI-driven sustainable drug discovery.
REFERENCES (51)
1.
Albani, F.G., Alghamdi, S.S., Almutairi, M.M., Alqahtani, T., 2025. Artificial intelligence-driven innovations in oncology drug discovery: transforming traditional pipelines and enhancing drug design. Drug Design, Development and Therapy. 19, 5685–5707. https://doi.org/10.2147/DDDT.S....
2.
Atanasov, A.G., Zotchev, S.B., Dirsch, V.M., Supuran, C.T., 2021. Natural products in drug discovery: advances and opportunities. Nature Reviews Drug discovery. 20(3), 200–216. https://doi.org/10.1038/s41573....
3.
Bagdad, Y., Miteva, M.A., 2024. Recent applications of artificial intelligence in discovery of new antibacterial agents. Advances and Applications in Bioinformatics and Chemistry: AABC. 17, 139–157. https://doi.org/10.2147/AABC.S....
4.
Bao, H., Zhao, J., Zhao, X., Zhao, C., Lu, X., Xu, G., 2023. Prediction of plant secondary metabolic pathways using deep transfer learning. BMC Bioinformatics. 24(1), 348. https://doi.org/10.1186/s12859....
5.
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., Garcia-Fandino, R., 2023. The role of AI in drug discovery: challenges, opportunities, and strategies. Pharmaceuticals (Basel, Switzerland). 16(6), 891. https://doi.org/10.3390/ph1606....
6.
Chen, L., Lu, W., Wang, L., Xing, X., Chen, Z., Teng, X., Zeng, X., Muscarella, A.D., Shen, Y., Cowan, A., McReynolds, M.R., Kennedy, B.J., Lato, A.M., Campagna, S.R., Singh, M., Rabinowitz, J.D., 2021. Metabolite discovery through global annotation of untargeted metabolomics data. Nature Methods. 18(11), 1377–1385. https://doi.org/10.1038/s41592....
7.
Dara, S., Dhamercherla, S., Jadav, S.S., Babu, C.M., Ahsan, M.J., 2022. Machine learning in drug discovery: a review. Artificial Intelligence Review. 55(3), 1947–1999. https://doi.org/10.1007/s10462....
8.
Di Lascio, E., Gerebtzoff, G., Rodriguez-Perez, R., 2023. Systematic evaluation of local and global machine learning models for the prediction of ADME properties. Molecular Pharmaceutics. 20(3), 1758–1767. https://doi.org/10.1021/acs.mo....
9.
Dias, D.A., Urban, S., Roessner, U., 2012. A historical overview of natural products in drug discovery. Metabolites. 2(2), 303–336. https://doi.org/10.3390/metabo....
10.
Du, P., Fan, R., Zhang, N., Wu, C., Zhang, Y., 2024. Advances in integrated multi-omics analysis for drug–target identification. Biomolecules. 14(6), 692. https://doi.org/10.3390/biom14....
11.
Ferreira, F.J., Carneiro, A.S., 2025. AI-driven drug discovery: a comprehensive review. ACS Omega. 10(23), 23889–23903. https://doi.org/10.1021/acsome....
12.
Fu, C., Chen, Q., 2025. The future of pharmaceuticals: artificial intelligence in drug discovery and development. Journal of Pharmaceutical Analysis. 15(8), 101248. https://doi.org/10.1016/j.jpha....
13.
Gangwal, A., Lavecchia, A., 2025. Artificial intelligence in natural product drug discovery: current applications and future perspectives. Journal of Medicinal Chemistry. 68(4), 3948–3969. https://doi.org/10.1021/acs.jm....
14.
Garg, P., Singhal, G., Kulkarni, P., Horne, D., Salgia, R., Singhal, S.S., 2024. Artificial Intelligence–driven computational approaches in the development of anticancer drugs. Cancers. 16(22), 3884. https://doi.org/10.3390/cancer....
15.
Han, H., Shaker, B., Lee, J. H., Choi, S., Yoon, S., Singh, M., Basith, S., Cui, M., Ahn, S., An, J., Kang, S., Yeom, M.S., Choi, S., 2025. Employing Automated Machine Learning (AutoML) methods to facilitate the in silico ADMET properties prediction. Journal of Chemical Information and Modeling. 65(7), 3215–3225. https://doi.org/10.1021/acs.jc....
16.
Hannigan, G.D., Prihoda, D., Palicka, A., Soukup, J., Klempir, O., Rampula, L., Durcak, J., Wurst, M., Kotowski, J., Chang, D., Wang, R., Piizzi, G., Temesi, G., Hazuda, D.J., Woelk, C.H., Bitton, D.A., 2019. A deep learning genome-mining strategy for biosynthetic gene cluster prediction. Nucleic Acids Research. 47(18), e110. https://doi.org/10.1093/nar/gk....
17.
Hou, X., Sun, M., Bao, T., Xie, X., Wei, F., Wang, S., 2020. Recent advances in screening active components from natural products based on bioaffinity techniques. Acta Pharmaceutica Sinica B. 10(10), 1800–1813. https://doi.org/10.1016/j.apsb....
19.
Huang, Z., Denti, P., Mistry, H., Kloprogge, F., 2024. Machine learning and artificial intelligence in PK-PD modeling: fad, friend, or foe? Clinical Pharmacology and Therapeutics. 115(4), 652. https://doi.org/10.1002/cpt.31....
21.
Jia, C.Y., Li, J. Y., Hao, G.F., Yang, G.F., 2020. A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug Discovery Today. 25(1), 248–258. https://doi.org/10.1016/j.drud....
22.
Jiang, W., Ye, W., Tan, X., Bao, Y.J., 2025. Network-based multi-omics integrative analysis methods in drug discovery: a systematic review. BioData Mining. 18(1), 27. https://doi.org/10.1186/s13040....
23.
Klukowski, P., Riek, R., Güntert, P., 2025. Machine learning in NMR spectroscopy. Progress in Nuclear Magnetic Resonance Spectroscopy. 148–149, 101575. https://doi.org/10.1016/j.pnmr....
24.
Luo, Y., Zheng, X., Qiu, M., Gou, Y., Yang, Z., Qu, X., Chen, Z., Lin, Y., 2025. Deep learning and its applications in nuclear magnetic resonance spectroscopy. Progress in Nuclear Magnetic Resonance Spectroscopy. 146–147, 101556. https://doi.org/10.1016/j.pnmr....
25.
Meng, F., Zhang, G., Xiao, W., Mao, Y., Shu, Y., Yang, X., Xu, G., Tang, X., Zhang, M., Liu, Z., Zhang, X., You, S., Wang, B., Yu, Z., Chen, S., Chen, W., 2025. MedMeta: an AI-enabled and genomics-based database for functional profiling of secondary metabolites in medicinal species. Engineering. https://doi.org/10.1016/j.eng.....
26.
Merk, D., Grisoni, F., Friedrich, L., Schneider, G., 2018. Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators. Communications Chemistry. 1(1), 68.
27.
Moingeon, P., Kuenemann, M., Guedj, M., 2022. Artificial intelligence-enhanced drug design and development: toward a computational precision medicine. Drug Discovery Today. 27(1), 215–222. https://doi.org/10.1016/j.drud....
28.
Mungwari, C.P., King’ondu, C.K., Sigauke, P., Obadele, B.A., 2024. Conventional and modern techniques for bioactive compounds recovery from plants. Scientific African. 27(1), e02509. https://doi.org/10.1016/j.scia....
29.
Newman, D.J., Cragg, G.M., 2020. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. Journal of Natural Products. 83(3), 770–803.
30.
Obaido, G., Mienye, I.D., Egbelowo, O.F., Emmanuel, I.D., Ogunleye, A., Ogbuokiri, B., Mienye, P., Aruleba, K., 2024. Supervised machine learning in drug discovery and development: algorithms, applications, challenges, and prospects. Machine Learning with Applications. 17, 100576. https://doi.org/10.1016/j.mlwa....
31.
Ocana, A., Pandiella, A., Privat, C., Bravo, I., Luengo-Oroz, M., Amir, E., Gyorffy, B., 2025. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomarker Research. 13(1), 45. https://doi.org/10.1186/s40364....
32.
Palazzotto, E., Weber, T., 2018. Omics and multi-omics approaches to study the biosynthesis of secondary metabolites in microorganisms. Current Opinion in Microbiology. 45, 109–116. https://doi.org/10.1016/j.mib.....
33.
Patel, L., Shukla, T., Huang, X., Ussery, D.W., Wang, S., 2020. Machine learning methods in drug discovery. Molecules (Basel, Switzerland). 25(22), 5277. https://doi.org/10.3390/molecu....
34.
Pathan, I., Raza, A., Sahu, A., Joshi, M., Sahu, Y., Patil, Y., Raza, M.A., 2025. Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction. Medicine in Drug Discovery. 28, 100223. https://doi.org/10.1016/j.medi....
35.
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., Tekade, R.K., 2021. Artificial intelligence in drug discovery and development. Drug Discovery Today. 26(1), 80–93. https://doi.org/10.1016/j.drud....
36.
Qadri, Y.A., Shaikh, S., Ahmad, K., Choi, I., Kim, S.W., Vasilakos, A.V., 2025. Explainable artificial intelligence: a perspective on drug discovery. Pharmaceutics. 17(9), 1119. https://doi.org/10.3390/pharma....
37.
Rai, S., Pandey, P., Gupta, A., Sharma, S., 2023. Virtual screening and high-throughput screening in drug discovery. European Journal of Military Studies. 13(4).
38.
Sarkar, C., Das, B., Rawat, V.S., Wahlang, J.B., Nongpiur, A., Tiewsoh, I., Lyngdoh, N.M., Das, D., Bidarolli, M., Sony, H.T., 2023. Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences. 24(3), 2026. https://doi.org/10.3390/ijms24....
39.
Sathya, S., Manikandan, S., Rajeswari, V.R., 2025. Natural products with artificial intelligence: a revolution in drug discovery. International Journal of Research in Pharmacy and Allied Science. 4(1), 45–62.
40.
Simoben, C.V., Babiaka, S.B., Moumbock, A.F.A., Namba-Nzanguim, C.T., Eni, D.B., Medina-Franco, J.L., Günther, S., Ntie-Kang, F., Sippl, W., 2023. Challenges in natural product-based drug discovery assisted with in silico-based methods. RSC Advances. 13(45), 31578–31594. https://doi.org/10.1039/d3ra06....
41.
Singh, S., Kumar, R., Payra, S., Singh, S.K., 2023. Artificial intelligence and machine learning in pharmacological research: bridging the gap between data and drug discovery. Cureus. 15(8), e44359. https://doi.org/10.7759/cureus....
42.
Siramshetty, V.B., Xu, X., Shah, P., 2024. Artificial intelligence in ADME property prediction. Methods in Molecular Biology (Clifton, N.J.). 2714, 307–327. https://doi.org/10.1007/978-1-....
43.
Tang, A., 2023. Machine learning for pharmacokinetic/pharmacody-namic modeling. Journal of Pharmaceutical Sciences. 112(5), 1460–1475. https://doi.org/10.1016/j.xphs....
44.
Thomford, N.E., Senthebane, D.A., Rowe, A., Munro, D., Seele, P., Maroyi, A., Dzobo, K., 2018. Natural products for drug discovery in the 21st century: innovations for novel drug discovery. International Journal of Molecular Sciences. 19(6), 1578. https://doi.org/10.3390/ijms19....
45.
Tiwari, P.C., Pal, R., Chaudhary, M.J., Nath, R., 2023. Artificial intel-ligence revolutionizing drug development: exploring opportunities and challenges. Drug Development Research. 84(8), 1652–1663. https://doi.org/10.1002/ddr.22....
46.
Vilar, S., Costanzi, S., 2012. Predicting the biological activities through QSAR analysis and docking-based scoring. Methods in Molecular Biology (Clifton, N.J.). 914, 271–284. https://doi.org/10.1007/978-1-....
47.
Vora, L.K., Gholap, A.D., Jetha, K., Thakur, R.R.S., Solanki, H.K., Chavda, V.P., 2023. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 15(7), 1916. https://doi.org/10.3390/pharma....
48.
Walker, A.S., Clardy, J., 2021. A machine learning bioinformatics method to predict biological activity from biosynthetic gene clus-ters. Journal of Chemical Information and Modeling. 61(6), 2560–2571. https://doi.org/10.1021/acs.jc....
49.
Wei, W., Liao, Y., Wang, Y., Wang, S., Du, W., Lu, H., Kong, B., Yang, H., Zhang, Z., 2022. Deep learning-based method for compound identification in NMR spectra of mixtures. Molecules. 27(12), 3653. https://doi.org/10.3390/molecu....
50.
Xu, R., Zhu, J., 2025. Unveiling the dark matter of the metabo-lome: a narrative review of bioinformatics tools for LC-HRMS-Based Compound Annotation. Talanta. 295, 128327. https://doi.org/10.1016/j.tala....
51.
Zhang, L., Liu, Y., Chen, K., Yue, Q., Wang, C., Xie, L., Molnár, I., Xu, Y., 2025. Genome mining of tailoring enzymes from biosyn-thetic gene clusters for synthetic biology: a case study with fungal methyltransferases. Metabolic Engineering. 92, 125–135. https://doi.org/10.1016/j.ymbe....
Share
RELATED ARTICLE
| eISSN: | 2583-1194 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
