Anticancer Phytochemicals in the Age of AI: Target Vulnerability, Tumor Pathway Dependency, Molecular Selectivity, and Safety-Aware Candidate Prioritization

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Anticancer phytochemicals occupy an important but methodologically challenging position in contemporary cancer pharmacology because plant-derived compounds may show tumor-relevant biological activity while also carrying uncertainty regarding target engagement, pathway context, selectivity, safety, exposure, and translational readiness. This Original Conceptual Framework Article proposes an AI-assisted framework for prioritizing anticancer phytochemical candidates as research hypotheses rather than therapeutic recommendations. The framework integrates phytochemical identity, compound class, natural product source, chemical characterization, cancer-context definition, target vulnerability, target confidence, oncogenic pathway relevance, tumor pathway dependency, molecular-signature evidence, molecular selectivity, tumor-normal contrast, ADMET evidence, toxicity signals, off-target liability, exposure plausibility, evidence provenance, evidence grading, uncertainty, and validation gaps. Its central logic is that AI can assist chemical annotation, target prediction, pathway inference, molecular-signature interpretation, safety-aware filtering, and candidate ranking only when outputs are interpreted through tumor biology and experimental validation needs. The proposed framework distinguishes AI-predicted mechanisms from experimentally supported target evidence, cytotoxicity from tumor-selective anticancer relevance, and preclinical evidence from clinical claims. The main contribution is a structured prioritization logic that links computational screening to oncology-informed interpretation, safety constraints, and translational boundaries. The framework is intended to support responsible research prioritization, clearer reporting, and validation planning without implying clinical anticancer efficacy.