Maturity Pathways for Computational Phytopharmacology: From Descriptive Compound Databases to Predictive, Explainable, and Translational Discovery Systems

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Computational phytopharmacology is increasingly shaped by compound databases, curated natural product repositories, predictive models, knowledge graphs, explainable artificial intelligence, safety informatics, and translational decision-support concepts. However, the field often evaluates individual methods without a structured account of how computational systems mature from descriptive resources into evidence-aware discovery environments. This article proposes an original maturity pathway for computational phytopharmacology systems. The model defines staged progression from descriptive compound databases to curated and linked phytopharmacology data systems, predictive computational discovery systems, explainable evidence-aware decision-support systems, and translational validation-linked discovery systems. The proposed maturity logic emphasizes that higher maturity is not achieved merely by expanding database size or increasing model complexity. Instead, maturity requires improved data quality, provenance, chemical and botanical annotation, target and pathway evidence, ADMET and safety awareness, model validation, uncertainty communication, explainability, evidence grading, expert review, governance, and clearly bounded decision-support roles. The article distinguishes lookup-oriented resources from predictive hypothesis-generation systems and from translationally useful platforms that remain dependent on experimental and clinical validation. The maturity model offers a conceptual framework for researchers, database developers, AI model designers, pharmacologists, phytomedicine scholars, and translational scientists seeking to assess computational phytopharmacology systems without overstating predictive, mechanistic, or clinical claims.