Reference Architectures for AI-Enabled Phytopharmaceutical Platforms: Data Layers, Model Layers, Evidence Layers, and Decision-Support Interfaces

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AI-enabled phytopharmaceutical platforms are emerging as computational infrastructures for organizing natural product data, interpreting pharmacological mechanisms, screening safety-relevant signals, and supporting translational research prioritization. However, many current computational workflows remain fragmented across databases, predictive models, network analyses, literature-mining tools, and dashboard outputs, making it difficult to distinguish curated evidence, predicted outputs, validation status, and decision-support boundaries. This article proposes an original reference architecture for AI-enabled phytopharmaceutical platforms. The architecture organizes platform design across data acquisition, ingestion, curation, provenance tracking, knowledge integration, model execution, evidence grading, explainability, validation gates, governance controls, expert review, and decision-support interfaces. Its data layer focus includes botanical identity, preparation context, phytochemical composition, bioactivity evidence, target and pathway data, omics information, ADMET and toxicity data, herb–drug interaction signals, clinical evidence where available, literature evidence, and metadata provenance. Its model and evidence layer focus separates machine learning, natural language processing, knowledge graphs, network pharmacology, QSAR or ADMET prediction, target prediction, pathway inference, safety screening, uncertainty communication, and validation status. Its interface focus emphasizes research prioritization, mechanism-evidence review, safety-alert triage, translational readiness assessment, and validation planning without replacing expert judgment. The main contribution is a modular, reusable, governance-aware architecture for designing transparent, auditable, explainable, and validation-aware phytopharmaceutical research platforms.