Network Pharmacology Without Oversimplification: Designing a Mechanism-Centered Model for Polyherbal Formulations and Multi-Target Therapeutics

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Network pharmacology has become a prominent strategy for studying multi-component therapeutic systems, yet its application to polyherbal formulations often remains limited by static compound–target–pathway mapping. Polyherbal formulations are chemically heterogeneous, preparation-dependent, exposure-conditioned systems in which botanical identity, plant part, processing, extraction, dose, metabolism, tissue distribution, and temporal exposure may determine whether a constituent is pharmacologically relevant. This article proposes a mechanism-centered network pharmacology model for polyherbal formulations and multi-target therapeutics. The model addresses oversimplified practices that equate compound occurrence with active exposure, predicted targets with validated engagement, protein–protein interaction hubs with causal importance, pathway enrichment with pathway modulation, and multi-target overlap with synergy. Instead, the proposed framework organizes polyherbal modeling through formulation composition, exposure and metabolic state, graded compound–target evidence, directional regulatory interaction, context-specific mechanism modules, cross-module therapeutic logic, and validation and translational evidence. The model distinguishes measured, curated, predicted, inferred, text-derived, and validated evidence states, while preserving activating, inhibitory, modulatory, associative, and unknown-direction relationships as non-equivalent edge types. It also separates additivity, conditional synergy, redundancy, complementarity, compensatory buffering, antagonism, exposure competition, target convergence, and target divergence as alternative therapeutic logics rather than assuming beneficial polypharmacology by default. Validation gates are proposed to prevent unsupported mechanism claims and to require composition integrity, exposure plausibility, target evidence, directionality, context specificity, module coherence, multi-target logic assessment, and experimental challenge before translational interpretation. The central contribution is a revisable, uncertainty-aware model that treats network pharmacology as a disciplined mechanism-hypothesis system rather than a visual demonstration of therapeutic validity.