Strategic Pathways for AI-Enabled Phytopharmaceutical Translation: Data Infrastructure, Model Validation, Mechanistic Evidence, Regulatory Alignment, and Clinical Adoption

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AI-enabled phytopharmaceutical translation is emerging at the intersection of natural product research, computational pharmacology, translational science, regulatory interpretation, and responsible clinical innovation. Although artificial intelligence may support compound prioritisation, target prediction, evidence integration, and safety-aware hypothesis generation, translation requires more than predictive performance. This original strategic roadmap article proposes a staged framework for moving AI-enabled phytopharmaceutical evidence from discovery-oriented outputs toward responsible translational planning. The roadmap emphasises interoperable data infrastructure, FAIR chemical and biological records, provenance, natural product identity, chemical annotation, bioactivity evidence, safety data, and clinical evidence traceability. It further defines model validation requirements, including internal testing, external validation, benchmarking, uncertainty communication, explainability, bias assessment, and generalisability. Mechanistic evidence is positioned as a plausibility layer requiring target evidence, pathway evidence, experimental validation, exposure relevance, ADMET interpretation, and safety validation, while regulatory alignment is framed as evidence-to-claim mapping rather than approval. Clinical adoption is treated as a later-stage pathway requiring workflow fit, clinician trust, human oversight, patient safety safeguards, implementation monitoring, and pharmacovigilance readiness. The main contribution is a structured strategic roadmap that links data readiness, model credibility, mechanistic plausibility, safety interpretation, regulatory logic, clinical workflow integration, governance safeguards, and continuous evidence updating while avoiding claims of clinical readiness without appropriate validation.