Table 2.

Summary of computational techniques in metabolomics for drug discovery.

Technique	Application	Key Tools	Strengths	Limitations	Ref.
Molecular docking	Ligand-target prediction	AutoDock, GOLD, SwissDock	Structural insight, fast screening	Accuracy depends on protein structure data	[88]
Network-based modeling	Pathway simulation, target ID	COBRA Toolbox, MetaFlux	Mechanistic interpretation	Requires curated metabolic models	[86]
Machine learning and AI	Biomarker discovery, drug response	DeepChem, scikit-learn	Handles high-dimensional data	Needs large, labeled datasets	[89]
Multiscale modeling	System-wide simulation	CellDesigner, COPASI	Cross-scale integration of omics data	Computationally intensive	[90]