Translational Readiness in AI-Discovered Phytotherapeutics: Evidence Gates from In Silico Discovery to Preclinical and Clinical Decision-Making

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AI-assisted discovery is increasingly used to identify phytochemicals, predict biological targets, prioritise mechanisms, and screen safety-related properties of natural product candidates. However, computational outputs can easily be overinterpreted when they are not connected to transparent translational criteria. This article develops an original translational readiness framework for AI-discovered phytotherapeutics, positioning in silico evidence as hypothesis-generating rather than clinically confirmatory. The framework organises candidate advancement through staged evidence gates that begin with botanical and phytochemical identity, data provenance, model transparency, target plausibility, molecular docking, molecular dynamics, network pharmacology, ADMET screening, reproducibility checks, and mechanistic hypothesis quality. It then connects these computational gates to preclinical validation requirements, including in vitro confirmation, mechanism testing, target engagement, pathway validation, in vivo relevance, pharmacokinetic evidence, formulation standardisation, toxicity assessment, interaction screening, and reproducibility controls. The clinical decision pathway component emphasises that phytotherapeutic candidates should not enter clinical interpretation unless evidence quality, safety, uncertainty, patient context, therapeutic indication, interaction risk, formulation consistency, and clinical evidence alignment are explicitly evaluated. The main contribution is a staged evidence-gate model that clarifies when AI-discovered phytotherapeutic candidates should advance, pause, or be revised. The framework is designed to reduce translational overclaiming, distinguish computational novelty from biological readiness, and support responsible movement from in silico discovery toward preclinical testing and cautious clinical decision-making.