Emerging strategies for plant-derived anti-inflammatory drug discovery (IMAGE)
Caption
Network pharmacology and multi-omics are used to prioritize candidate targets, pathways, biomarkers, and treatment-associated cellular responses. Activity-linked fractionation and molecular networking improve dereplication and link chemical features with bioactivity in complex extracts. Conventional structure-based virtual screening prioritizes candidates using target structures, molecular docking, and predefined scoring functions, whereas AI-based methods include ligand-based machine learning, deep-learning scoring, active learning, and generative molecular design. AI predictions require careful consideration of training-data leakage, stereochemistry, protonation and tautomeric states, conformational complexity, applicability domain, and prospective experimental hit rates. Advanced preclinical models, including three-dimensional (3D) co-cultures, organoids, and organ-on-chip systems, provide more disease-relevant settings for phenotypic and mechanistic testing. Drug delivery and formulation strategies, including extracellular vesicles (EV), lipid-based systems, and carrier-free nanoparticles, can improve stability, bioavailability, and target-site exposure. These approaches span candidate discovery, experimental validation, pharmacokinetic optimization, and preclinical development. Created with BioRender.com under a publication license.
Credit
Prof. Weidong Zhang from Naval Medical University, China Image source link: https://link.springer.com/article/10.1007/s44466-026-00059-9
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