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

## TL;DR

This paper uses computer modeling to find natural compounds and plants that may help treat gastric cancer.

## Contribution

A novel in silico ensemble-based modeling strategy was developed to identify bioactive molecules from natural sources against gastric cancer.

## Key findings

- Ensemble models improved molecule identification by 12–15 times compared to random selection.
- 340 molecules from bioactive classes were prioritized, including known anticancer compounds.
- Plant genera Taxus, Glycyrrhiza, Elaphoglossum, and Seseli were identified as relevant sources.

## Abstract

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.

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.

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.

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.