Egieyeh, Samuel AyodeleNtie‑Kang, FideleBetow, Jude.Y2026-10-192026-10-192026Ntie-Kang, F., Betow, J.Y., Eni, D.B., Metuge, J.A., Adriano, A., Chama, M.A., Bamal, H.D., Kedi, P.B.E., Campos, L.M., Abdullahi, A.S. and Medina, G.F.F., 2026. Computational applications in secondary metabolite discovery (CAiSMD) 2026: focus on secondary metabolites towards drug discovery. Journal of Cheminformatics, 18(1), p.122.https://doi.org/10.1186/s13321-026-01280-9https://hdl.handle.net/10566/25686The Computational Applications in Secondary Metabolite Discovery (CAiSMD) 2026 workshop was held from 24 to 26 March 2026 in a hybrid format, combining onsite participation at the University of Buea Center for Drug Discovery with global virtual attendance. Building on previous editions, CAiSMD continues to promote the application of bio/cheminformatics and computational chemistry in natural product-based discovery, with a strong emphasis on capacity building for researchers in resource-limited settings. Methods The scientific program featured three keynote lectures, seven oral presentations, two hands-on training sessions, two roundtable discussions, and a session dedicated to Young Investigators. Key themes included chemoinformatics resources (databases, webservers, packages, codes, etc.) for natural product research, antimicrobial drug discovery, integrated computational bioprospection, virtual screening, network pharmacology, and multi-target drug design. The hands-on sessions provided practical training in chemical space exploration and virtual screening using the Latin American and African natural products databases. Results A major highlight was the Young Investigator session, which showcased innovative research from emerging scientists (MSc students, PhD students, and early postdocs) across diverse therapeutic areas, emphasizing the growing adoption of computational approaches worldwide. Interactive discussions further addressed challenges related to data accessibility, interdisciplinary collaboration, and training needs. Complemented with relevant experimental models, the introduction of bioinformatics, and computational systems biology have accelerated therapeutic innovations through the elimination of time-consuming, expensive, challenging, and inefficient processes that are consistent with the conventional drug discovery systems.enNatural productsDrug discoveryCapacity buildingCheminformaticsComputational chemistryArticle