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Figure 1. 

Framework for the identification of direct targets of natural products. Natural products or bioactive compounds are isolated from natural sources and subjected to activity-guided purification, with tiliroside shown as an illustrative example. Candidate targets can first be nominated using omics-based analyses, artificial intelligence-assisted structure prediction, molecular docking, and network pharmacology. Experimental target-fishing strategies are subsequently divided into label-free and label-based approaches. (a) Label-free strategies detect ligand-induced changes in protein stability, protease susceptibility, or local conformation. DARTS identifies proteins protected from proteolysis after ligand binding; CETSA evaluates ligand-induced alterations in thermal stability; TPP extends thermal stability analysis to the proteome scale; and LiP-MS detects changes in local protease accessibility and maps ligand-responsive protein regions. (b) Label-based strategies use activity- or affinity-based chemical probes, photoaffinity labeling, click chemistry, and biotin-streptavidin enrichment to capture compound-interacting proteins. Depending on the target-fishing strategy, candidate proteins are recovered by affinity enrichment or collected from soluble or proteolytic fractions and subsequently identified by immunoblotting or LC-MS/MS. Candidate targets should be further evaluated using orthogonal binding and functional validation assays.

Figure 2. 

Framework for validating direct binding and functional causality. (a) Biophysical and cellular target-engagement assays offer complementary evidence for compound–protein interactions. SPR, MST, ITC, and NMR assess binding kinetics, affinity, thermodynamics, and structural changes, respectively. CETSA measures target engagement in cells or lysates via thermal stability shifts, but should be combined with purified-protein biophysical assays to confirm direct binding. (b) Orthogonal biochemical validation uses affinity pull-down, competition assays, functional readouts, and site-directed mutagenesis to confirm binding specificity and identify critical residues. Competition by excess unmodified compound supports specific binding, while loss of binding or activity after mutation indicates a defined binding site.

Figure 3. 

Single-cell and spatial multi-omics platforms for resolving the pharmacological mechanisms of natural products. (a) scRNA-seq identifies drug-responsive cell populations and transcriptional states after quality control, normalization, integration, clustering, and annotation. (b) scATAC-seq and single-cell multiome approaches profile chromatin accessibility, transcription-factor motifs, and regulatory links between accessible elements and gene expression. (c) Spatial transcriptomics and spatial multi-omics retain tissue architecture and map treatment-responsive pathways or cellular interactions to defined pathological niches. These modalities provide contextual and mechanistic evidence but do not independently establish direct compound-target binding.

Figure 4. 

AI-assisted candidate prioritization as an upstream component of natural product research. (a) Conventional screening uses prior knowledge and large experimental libraries to select compounds, followed by cellular and animal validation. (b) AI-assisted screening integrates chemical, ADMET, target, disease, and multi-omics information using machine-learning or deep-learning models to prioritize candidates before experiments. This workflow reduces the initial search space but does not replace target-engagement or efficacy validation.

Figure 5. 

AI-assisted drug-target identification and target-based drug screening. (a) Drug-based target prediction, which integrates 1D, 2D, and 3D similarity information of drugs together with multi-source data such as known ligands, protein sequences, binding-pocket features, and knowledge graphs to identify potential targets, followed by molecular docking to validate candidate hits. (b) Target-based drug screening, which uses protein sequence and three-dimensional structural information, combined with SMILES representations, molecular fingerprints, and other features, to build predictive models that score and rank candidate compounds, thereby enabling efficient virtual screening and prioritization of promising drug candidates.

Figure 6. 

Evidence-informed closed-loop framework for natural product target discovery.