The study selection process was conducted in accordance with the key elements of the PRISMA guidelines, particularly the systematic screening and selection procedures. Two researchers independently screened titles/abstracts and subsequently assessed full-text articles for eligibility. Data from included studies were extracted independently by the same two researchers. Discrepancies at any stage were resolved through consensus or consultation with a third reviewer. This workflow was designed to ensure reproducibility and minimize bias, as illustrated in Figure 1.

To address inconsistent terminology for core discoveries (e.g., “key targets,” “core targets”), we developed a priori glossaries that compiled a comprehensive set of relevant phrases for “key metabolites” and “key targets” (Supplementary File 1), identified through an initial review of the literature. Any list presented under these predefined headings was considered to represent the study’s key findings. This approach provided a reproducible rule set that captured the vast majority of reported key elements.

Data were extracted separately for key metabolites, targets, and pathways. Extraction was contingent upon the study reporting no more than 30 items per category, applying the standardized glossary (Supplementary File 1). For pathways, we specifically extracted those visually presented as key findings in Kyoto Encyclopedia of Genes and Genomes enrichment analysis plots or explicitly listed in results. If a study presented multiple non-identical lists of key elements, the smaller, more refined set was extracted to capture the authors’ prioritized findings.

We focused this rigorous cleaning on the top 20 most frequent metabolites and targets. For these high-priority elements, we curated lists of their most common synonyms from authoritative databases (PubChem for metabolites, UniProt for targets) (see Supplementary File 1). For example, for a prevalent metabolite like quercetin, we unified its numerous high-frequency synonyms (e.g., “Sophoretin,” “Meletin”) under the standard name “Quercetin”. This targeted approach was implemented to mitigate the impact of nomenclature variation on the frequency counts of high-priority elements.

The homogeneity in NA findings originates from systemic biases embedded within foundational resources. This initial bias occurs across two interdependent domains: the chemical space of metabolites and the biological space of targets. Notably, these issues persist despite established NA guidelines advocating for high-quality data and comprehensive pharmacokinetic evaluation (Wang Z. Y. et al., 2022). These biases do not merely narrow the field of view; they systematically seed the analytical pipeline with candidates prone to being false positives or pharmacologically irrelevant predictions, a risk magnified when dealing with complex mixtures.

If biased inputs provide homogenized and contaminated ingredients, the prevailing modeling paradigm acts as the recipe that systematically converts these flaws into a seemingly coherent, yet pharmacologically misleading, narrative. This paradigm is fundamentally “context-indifferent.” It not only overlooks the dynamic and tissue-specific nature of biological systems but, more critically, lacks the discriminatory power to separate genuine pharmacological signals from the noise of false positives and non-specific interactions seeded at the input level.

The sequential action of input- and model-level biases leads to a convergent output: the recurrent identification of a narrow set of compounds, targets, and pathways. While this convergence intersects with some established biology [e.g., flavonoids modulating broad signaling pathways (Mansuri et al., 2014; Li et al., 2020)], the core issue is its systematic over-amplification by a pipeline that is highly prone to generating predictable results. A fundamental challenge is that such computational approaches will produce a set of “key” elements regardless of the specific query, which can lead to over-interpretation if not critically contextualized.

This convergent pipeline operates within a broader research ecosystem that inadvertently reinforces it, forming a self-reinforcing cycle (Figure 2). The cycle is propelled by three interdependent feedback mechanisms that can gradually solidify preliminary computational findings into perceived biological truths, often without the rigorous pharmacological scrutiny they require:

Our analysis demonstrates that shifting from database-derived predictions to empirically grounded data is a decisive strategy for reducing the reliance on false leads and pharmacologically questionable predictions. This shift, as shown in Table 2, concomitantly markedly reduces the prevalence of homogenized elements at the field level. For instance, quercetin’s identification as a key metabolite dropped from 71.9% to 42.3%, and AKT1’s prevalence as a key target fell from 54.9% to 19.6%.

To pinpoint the mechanism behind this improvement, we conducted a controlled re-analysis of a study on Taohong Siwu Decoction (Li et al., 2025). We created a parallel workflow that differed only in two inputs: (1) using a TCMSP-derived constituent list rather than the experimentally verified blood-absorbed compounds, and (2) selecting disease-relevant targets from generic databases instead of intersecting predictions with experimental metabolomic data.

The divergence was stark and systematic. The database-driven path prioritized “chemical ghosts”—specifically quercetin, luteolin, kaempferol, baicalein, and β-sitosterol—none of which were detected in the original plasma metabolome. In contrast, the original study identified in vivo-confirmed constituents like ferulic acid and vanillic acid as key bioactives. Crucially, these “ghost” compounds are archetypal homogenized elements. They exhibited extraordinarily high numbers of predicted targets (223, 125, 119, 118, and 109, respectively), with an average of 139 predicted interactions—far exceeding the background average of ∼32 per compound in our dataset. These compounds exemplify how database-centric approaches can systematically prioritize molecules that are not only homogenized but are also high-risk candidates for generating false-positive pharmacological narratives due to their absence in vivo and/or PAINS-like properties. Consequently, the inferred core target sets diverged fundamentally. The original, empirically-anchored analysis identified a target set (e.g., JUN, PTGS2, BCL2, ESR1, PPARG). The database path yielded a distinct set dominated by ubiquitous, high-degree proteins (TP53, HSP90AA1, AKT1, JUN, EP300). Notably, AKT1—the most homogenized target in our census—was not linked to any experimentally confirmed constituents. It entered the network solely through interactions predicted for the “ghost” flavonoids and other database-specific compounds (e.g., beta-carotene).

It is important to note that this case study is illustrative rather than comprehensive. Its purpose is not to provide definitive proof for a specific formula’s mechanism, but to concretely demonstrate how methodological choices at the input stage (database vs. empirical data) can systematically divert the analytical trajectory toward or away from homogenized elements. This case demonstrates that the shift to empirical data is not merely a diversification tactic, but a critical strategy to bypass the false leads generated by database artifacts and PAINS-driven predictions, thereby grounding hypotheses in biologically observable events. Together, the field-level statistics and this mechanistic case study provide compelling, multi-level evidence for the proposed shift.

This evidence underscores the necessity of a paradigm shift in data acquisition, based on two core principles:

Prioritize In Vivo Exposure for Ingredient Screening. “In vivo detectability” should be a primary criterion. Techniques like Ultra High Performance Liquid Chromatography-Tandem Mass Spectrometry (UHPLC-MS/MS) should be used to identify key constituents, thereby circumventing database-driven “chemical ghosts” and ensuring that hypotheses are built on compounds with actual pharmacological opportunity (Zhang et al., 2021; Wei et al., 2022).

Anchor Target Identification in Pathophysiological Context. Target prioritization must integrate multi-omics data (e.g., transcriptomics, metabolomics) that reflect the specific disease context. This anchors target hypotheses in disease biology, helping to discriminate genuine therapeutic associations from the false-positive “hub” targets that arise from topology-driven analysis. (Chung and Kang, 2019).

To systematically implement these principles, we propose establishing a data evidence hierarchy within network analysis guidelines. This hierarchy would formally recognize the superior value of context-specific empirical data:

Highest Priority (Tier 1): Data from in vivo pharmacokinetic studies of the specific formula or herb, identifying prototype compounds and metabolites actually present in systemic circulation or target tissues.

Secondary Consideration (Tier 2): Constituent lists from generic phytochemical databases (e.g., TCMSP). When used, methodological limitations must be explicitly acknowledged—for example: “The compound list was sourced from database X, which is based on in vitro phytochemical profiles; the in vivo relevance and relative abundance of these compounds for the studied formula were not confirmed in this work.”

Such a framework encourages transparency and mandates that researchers justify their data sources while actively seeking higher-quality evidence. Studies should also report specific analytical methods (e.g., UHPLC-MS/MS) and any steps taken to cross-verify database entries, such as comparison with literature on absorbed components.

Building on this foundation, we advocate for a complementary, community-driven strategy of “co-development and enhancement.” This involves establishing shared data standards and incentivizing researchers to contribute validated empirical data to public platforms. The strategy focuses on two synergistic actions: (1) co-developing dedicated repositories for high-quality in vivo pharmacokinetic and multi-omics data, and (2) enhancing existing general databases by integrating this empirical evidence with higher confidence weighting, using clear annotations such as “In Vivo Verified” or “Tissue-Specific Expression.”

By adopting a clear evidence hierarchy and fostering open data collaboration, the research community can progressively counterbalance the overrepresentation of “high-frequency components” in predictive databases. This will steer network analysis toward a more dynamic, evidence-informed, and self-correcting knowledge foundation.

To disrupt the cycle of unreliable predictions, we advocate for a strategic, pharmacologically-informed application of artificial intelligence. AI should be leveraged not as a black-box oracle, but as a tool to explicitly address the identified sources of pharmacological irrelevance: screening for interference compounds, correcting for target bias, and modeling functional outcomes. This shifts AI’s role from a passive predictor to an active curator for pharmacologically plausible hypotheses, thereby increasing the likelihood that computational outputs will lead to meaningful experimental validation.

At the input stage, AI can directly counter “Flavonoid Centrality” by identifying and accounting for chemically promiscuous or assay-interfering compounds. Integrated platforms such as ChemFH exemplify this approach. ChemFH is an online tool that screens for frequent false positives—including colloidal aggregators, fluorescent compounds, and promiscuous molecules—using deep learning models trained on large datasets of known interferents (Shi et al., 2024). Incorporating such tools into virtual screening workflows allows researchers to algorithmically down-weight high-risk candidates, reducing the systematic over-prioritization of nonspecific bioactive compounds from the outset. Furthermore, AI-powered pharmacokinetic predictors (e.g., BioTransformer) facilitate a move beyond simplistic OB/DL filters by offering nuanced predictions of in vivo absorption and metabolic fate (Wishart et al., 2022; Fang et al., 2023). This helps prioritize a more diverse and pharmacologically realistic set of candidate constituents. To institutionalize this step, future methodological guidelines should encourage—or even mandate—the reporting of such screenings, requiring transparency on how potential FHCs were identified and handled.

Beyond input filtering, advanced AI models are essential for correcting the predictive biases that reinforce the “Hub-Target Core.” The field must shift from predicting binary interactions to modeling context-specific and functionally annotated relationships. Frameworks such as the Target-bias Aware Prediction Benchmark (TAPB) explicitly address prior bias toward highly studied targets in drug–target interaction models, providing a blueprint for debiased prediction (Lin et al., 2025). Concurrently, models like DrugAI predict not merely binding but also the direction of pharmacological effect (activation/inhibition), adding a crucial layer of functional context for target prioritization (Zhang et al., 2023). This allows researchers to evaluate targets based on their putative mechanistic role within a disease-relevant pathway, rather than on topological promiscuity alone. Furthermore, capturing the critical influence of structural heterogeneity on bioactivity—where slight chemical modifications can dramatically alter a compound’s effect—requires moving beyond simplistic molecular representations (Stumpfe and Bajorath, 2012). AI-driven models excel at this by learning from nuanced features such as 3D conformations and pharmacophores, offering a significant advantage over traditional linear notations (e.g., SMILES) in distinguishing between chemically similar yet pharmacologically distinct compounds, such as different flavonoids (Gu et al., 2025; An et al., 2022).

Finally, AI serves as the cornerstone for building the next-generation of equitable, evidence-weighted knowledge foundations—a critical step for long-term bias mitigation. Platforms like TCMBank and BATMAN-TCM exemplify this direction (Lv et al., 2023; Kong et al., 2024). TCMBank intelligently integrates and manually curates compound–target–disease relationships from vast literature and multiple databases, creating a consolidated resource that reduces the fragmentation and inherent biases of single-source repositories. BATMAN-TCM further incorporates multi-omics data to enable context-aware screening. On the technological front, automated literature-mining systems (leveraging models like BioBERT) can continuously scan and cross-verify findings, flagging inconsistencies for expert review and gradually cleansing the knowledge ecosystem of self-perpetuating errors (Peng et al., 2020; Lee et al., 2020; Hwang et al., 2021). To support transparency, the use of such integrated knowledge bases should be encouraged, and studies should report complete target lists to enable critical appraisal and meta-analysis.

In summary, by consciously directing AI’s capabilities toward input bias detection, debiased and functional prediction, and robust knowledge integration, we can transform it into a powerful engine for generating pharmacologically credible hypotheses. This structured, AI-informed strategy is essential for systematically dismantling the cycle of unreliable predictions and rebuilding a discovery pipeline whose outputs are both novel and primed for rigorous experimental confirmation.

To generate hypotheses that are both mechanistically insightful and readily testable, network analysis must advance from static snapshots toward a dynamic and context-aware modeling paradigm. This evolution addresses the core pharmacological limitation of current methods and would concomitantly reduce methodological homogeneity. This evolution is supported by emerging research and would benefit from a corresponding shift in methodological guidelines.

Strategically integrating multi-omics data is central to this transition. Moving beyond linear associations requires computational frameworks capable of fusing heterogeneous data types, such as transcriptomics, proteomics, and metabolomics. Two principal strategies show promise: mixed integration, which transforms diverse datasets into a unified analytical format, and hierarchical integration, which organizes data by biological scale (e.g., molecular, cellular, tissue) to respect inherent regulatory relationships (Liu et al., 2020; Mariette and Villa-Vialaneix, 2018; Picard et al., 2021). AI techniques—including multi-view learning and graph neural networks—are key to implementing these strategies, enabling models that reflect systemic complexity and can simulate drug action across varied contexts.

Pioneering frameworks illustrate this potential. SETComp uses deep learning to identify cell-specific mechanisms, moving beyond static hub-target assumptions (Wang et al., 2025). Herb-CMap integrates perturbed transcriptomic data with network propagation to reveal critical targets overlooked by conventional analyses (Wang et al., 2024). Platforms like POINT demonstrate how combining multi-omics networks with knowledge graphs can counteract biases inherent in single-layer approaches (He et al., 2025).

A critical observation, however, is that even advanced frameworks like Herb-CMap can still output homogenized elements—such as quercetin, AKT1, and PI3K-Akt signaling—when their input data are drawn from the narrow chemical-biological space common in the field. This critical observation underscores that algorithmic sophistication alone cannot rescue pharmacologically questionable inputs. It reinforces the imperative for the paradigm shift outlined in Section 4.3.1: enriching the pipeline with context-rich, dynamic data is a prerequisite for generating novel and reliable hypotheses, not just a technical enhancement.

To support this evolution, we propose several extensions to future methodological guidelines, grounded in the issues identified in our analysis:

Encourage temporally and conditionally resolved data. Where feasible, guidelines should promote the use of time-series or dose-response omics data. This would shift the focus from static comparisons toward modeling pharmacodynamic trajectories and contextual responses, thereby providing testable predictions about kinetic profiles and dose-dependencies that are central to pharmacological evaluation.

Advance beyond topology-only metrics. While useful, simple metrics like degree centrality often mistake broad connectivity for therapeutic relevance. Guidelines could emphasize context-aware measures—such as betweenness centrality (identifying network bottlenecks), eigenvector centrality (weighting influential nodes), or network propagation seeded with disease-specific data—to better prioritize targets with pathological specificity, and away from targets prioritized solely by their propensity to be false-positive hubs.

Promoting robust pathway interpretation. To counter the size bias of ORA, guidelines could encourage methods like Gene Set Enrichment Analysis (GSEA) or pathway topology analysis. When ORA is used, transparent reporting of background gene sets and discussion of potential biases should be encouraged to prevent the misinterpretation of statistical artifacts as biological insight.

Fostering iterative and transparent validation. Guidelines should reinforce the importance of context-aware experimental validation of computational predictions. Supporting an iterative cycle where results refine models, and reporting complete target lists and key parameters, would enhance reproducibility and enable community evaluation.

In summary, advancing toward dynamic, context-aware models is not a purely computational exercise. Its paramount value lies in generating hypotheses that are temporally resolved, dose-dependent, and mechanistically nuanced—attributes that make them inherently more testable and pharmacologically relevant than the static, homogenized outputs of current practices. This evolution from static to dynamic modeling is, therefore, not an optional upgrade but an essential re-engineering of the hypothesis generation process, designed to close the gap between computational prediction and actionable, pharmacologically rigorous experimental design.

To translate our conceptual framework into practice and enhance accessibility for groups with varying resources, we propose a phased roadmap structured around the core workflow of network analysis. This roadmap is designed to accommodate a spectrum of implementation levels—from a minimum viable approach that focuses on critical, resource-efficient interventions, to an ideal implementation that fully realizes the framework’s potential. Both pathways are designed with a common, overriding objective: to incrementally increase the pharmacological credibility and testability of the hypotheses generated. This means that even resource-constrained “minimum viable” steps should be explicitly chosen to mitigate the most egregious sources of false positives and biological irrelevance, paving a principled (if incremental) path toward more reliable discovery (Figure 3).

The goal of this stage is to transform a generic list of predicted interactions into a prioritized shortlist of targets that justify experimental investment. To counter the “Hub-Target Core,” target lists must be explicitly linked to the disease context, ensuring that limited experimental resources are directed toward testing targets with a higher prior probability of specific, mechanistically meaningful engagement.

It is non-negotiable and defines the ultimate purpose of the entire framework. It represents the essential transition from computational speculation to pharmacological evidence. The credibility of any network analysis study is contingent upon its commitment to this validation loop. This stage transforms computational “predictions” into pharmacological “evidence.” Its output is not merely a publication, but empirical data that retrospectively validates the computational workflow and prospectively enriches the community knowledge base.