Discovering molecules and plants with potential activity against gastric cancer: an in silico ensemble-based modeling analysis

Micaela Villacrés¹Alec Avila²Karina Jimenes-Vargas^2,3António Machado^4,5José M. Alvarez-Suarez^6,7*Eduardo Tejera^2,8*

• ¹Clínica San Cayetano, Quito, Ecuador
• ²Bio-Cheminformatics Research Group, Universidad de Las Américas, Quito, Ecuador
• ³Departament of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruña, Spain
• ⁴Universidade dos Açores, Faculdade de Ciências e Tecnologia, Departamento de Biologia, Centro de Biotecnologia dos Açores (CBA), Ponta Delgada, Portugal
• ⁵Universidad San Francisco de Quito USFQ, Colegio de Ciencias Biológicas y Ambientales COCIBA, Instituto de Microbiología, Laboratorio de Bacteriología, Quito, Ecuador
• ⁶Laboratorio de Investigación en Ingeniería en Alimentos (LabInAli), Departamento de Ingeniería en Alimentos, Colegio de Ciencias e Ingenierías, Universidad San Francisco de Quito (USFQ),, Quito, Ecuador
• ⁷Laboratorio de Bioexploración, Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito (USFQ),, Quito, Ecuador
• ⁸Facultad de Ingeniería y Ciencias Aplicadas. Universidad de Las Américas, Quito, Ecuador

Background: Gastric cancer (GC) remains a major global health burden despite advances in diagnosis and treatment. In recent years, natural products have gained increasing attention as promising sources of anticancer agents, including GC. Methods: In this study, we applied an in silico ensemble-based modeling strategy to predict compounds with potential inhibitory effects against four GC-related cell lines: AGS, NCI-N87, BGC-823, and SNU-16. Individual predictive models were developed using several algorithms and further integrated into two consensus ensemble multi-objective models. A comprehensive database of over 100,000 natural compounds from 21,665 plant species, was screened for validation and to identify potential molecular candidates. Results: The ensemble models demonstrated a 12–15-fold improvement in identifying active molecules compared to random selection. A total of 340 molecules were prioritized, many belonging to bioactive classes such as taxane diterpenoids, flavonoids, isoflavonoids, phloroglucinols, and tryptophan alkaloids. Known anticancer compounds, including paclitaxel, orsaponin (OSW-1), glycybenzofuran, and glyurallin A, were successfully retrieved, reinforcing the validity of the approach. Species from the genera Taxus, Glycyrrhiza, Elaphoglossum, and Seseli emerged as particularly relevant sources of bioactive candidates. Conclusions: While some genera, such as Taxus and Glycyrrhiza, have well-documented anticancer properties, others, including Elaphoglossum and Seseli, require further experimental validation. These findings highlight the potential of combining multi-objectives ensemble modeling with natural product databases to discover novel phytochemicals relevant to GC treatment.