Digital twin platform combining plant metabolic pattern AI with multi-omics data analysis for phytoeffector mode-of-action discovery

Overview

This solution offers a standardized omics-based digital twin for plants that predicts how small-molecule phytoeffectors influence plant metabolism and identifies their likely mode of action and protein targets. By combining plant primary and secondary metabolomics, proteomics, and literature-based smart data analysis within a metabolic network model, the platform provides a holistic view of how candidate compounds interact with crop plants. The approach is designed to accelerate the discovery and characterization of new crop protection agents, including herbicides and fungicides, by replacing slow, hypothesis-driven screening with pattern-based AI analysis.

The concept builds directly on a previously developed methodology and machine learning tool used to characterize the effects of anticancer drugs on human cancer cells, now extended and adapted to plant systems. The result is a decision-support tool for researchers and agritech companies seeking to understand compound behavior, optimize candidate selection, and reduce development risk.

Technical specifications

Core capabilities:

• Targeted plant primary and secondary metabolomics profiling under phytoeffector treatment
• Plant proteome analysis integrated with metabolic data
• Literature-based smart data analysis incorporating publicly available research and compound information
• Metabolic network modeling that links metabolite changes to pathway-level effects
• AI-supported pattern and pathway recognition to classify mode of action and predict protein targets
• Standardized workflows designed to produce reproducible, comparable datasets suitable for digital twin modeling

Key features:

• Extends a validated drug-discovery methodology from human cancer cell metabolomics to plant systems
• Multi-layer data integration across metabolomics, proteomics, and curated literature
• Designed to identify known modes of action and flag novel or unknown mechanisms

Technology readiness level

The underlying expertise in plant secondary metabolomics and phytoeffector response analysis is well established, with decades of experience in studying plant metabolism under stress and small-molecule influence. A closely related methodology and machine learning tool has already been developed for anticancer drug mode-of-action discovery and is currently under submission. To reach full digital twin readiness, the platform requires additional standardization of targeted plant primary metabolome measurements, integration of proteome data, and incorporation of literature-derived smart data analysis. The platform is therefore positioned at an advanced research stage, ready for collaborative development and validation with industry partners.