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Figure 1. 

Schematic diagram of AI-assisted target discovery in traditional Chinese medicine (TCM). The workflow integrates multiple data sources, including TCM databases, chemical space, multi-omics data, and clinical data, and infers potential TCM targets through ligand-driven, structure-driven, network-/graph-theory-based, multi-omics integration, large language model (LLM), and other AI-driven target prediction strategies. The predicted targets are then validated through biochemical binding experiments, cell-perturbation experiments, chemical proteomics, and in vivo pharmacological evaluation.

Figure 2. 

Integrated ligand-driven target discovery workflow for TCM compounds. (a) Traditional ligand-based discovery: compounds from TCM libraries are screened via chemical similarity and ligand-based virtual screening (LBVS)/quantitative structure–activity relationship (QSAR) models to rank potential bioactive molecules. (b) AI-enhanced ligand-driven models: graph- or simplified molecular input line entry system (SMILES)-based embeddings feed deep neural network (DNN), graph neural network (GNN), or transformer models to predict activity, explore chemical space, and enable scaffold hopping. Active learning cycles iteratively refine predictions with experimental feedback. (c) Key outputs: generates activity predictions, target links, scaffold hopping opportunities, prioritized leads, and informs rational compound design for experimental validation.

Figure 3. 

Integrated structure-driven target discovery workflow. (a) Traditional methods: combines molecular docking, structure-based virtual screening (SBVS), and molecular dynamics (MD) to evaluate ligand-protein binding, rank candidate compounds, and assess interaction stability over time. (b) AI-assisted methods: integrates AI-native docking, AI-enhanced SBVS, and AI-accelerated MD to predict binding affinities, efficiently prioritize compounds, and model dynamic interactions with deep learning refinement. (c) Output applications: highlights core outcomes of target discovery, including binding prediction, binding-site discovery, and dynamic validation, supporting experimental prioritization of TCM compounds while reducing visual complexity for journal readability.

Figure 4. 

Schematic workflow of AI-assisted network pharmacology for target discovery in traditional Chinese medicine (TCM). (a) Multi-source input data include genes, herbs, protein targets, literature, and databases. (b) Network construction integrates compound-target-disease associations with Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping. (c) AI-driven network analysis incorporates machine learning (ML), deep learning (DL), and graph neural networks (GNNs) for pattern recognition, feature extraction, and nonlinear mapping. (d) The resulting outputs include core hub targets, key signaling pathways, and mechanistic inference, thereby supporting target identification and biological interpretation in TCM.

Figure 5. 

Schematic overview of methodological challenges in multi-omics-based target discovery in traditional Chinese medicine (TCM). High-dimensional molecular associations are subject to confounding, temporal mismatch, and static omics snapshots, which complicate the distinction between correlation and causation and hinder the identification of high-confidence causal targets.

Figure 6. 

Schematic framework of generative AI and knowledge graph-based modeling for traditional Chinese medicine (TCM) target discovery. (a) Knowledge layer for transforming multi-source TCM information, including classical texts, electronic medical records (EMRs), databases, and multimodal data, into structured representations with the assistance of large language models (LLMs). (b) Construction of a TCM knowledge graph linking herbs, compounds, protein targets, and pathways. (c) AI application layer for evidence integration, hypothesis generation, reasoning, and downstream applications in pathway analysis, target discovery, and clinical decision support. (d) Key challenges and limitations, including hallucination, bias, validation gaps, and mechanistic uncertainty.

Figure 7. 

Hierarchical experimental framework for validation of AI-predicted targets from traditional Chinese medicine (TCM)-derived compounds. (a) Biochemical and biophysical binding validation via surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) using purified target proteins. (b) Intracellular target engagement profiling via chemoproteomics, including probe-based (activity-based protein profiling, ABPP; photoaffinity labeling, PAL) and label-free (thermal proteome profiling, TPP/CETSA; drug affinity responsive target stability, DARTS) approaches, for intracellular target profiling under near-physiological conditions. (c) Cellular functional and mechanistic validation via gene perturbation (RNA interference, siRNA/shRNA; CRISPR/Cas9-mediated gene knockout) to confirm target necessity for the observed pharmacological effects. (d) In vivo validation and safety assessment in animal models, evaluating target-dependent efficacy, pharmacokinetic-pharmacodynamic (PK-PD) profiles, tissue distribution, and safety. From left to right, the framework indicates increasing levels of biological complexity and evidence strength.