Explainable Molecular AI for Phytopharmacology: Linking Chemical Features, Target Interactions, Pathway Effects, and Therapeutic Decision Confidence

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Phytopharmacology increasingly uses molecular artificial intelligence to interpret plant-derived chemical diversity, predict bioactivity, infer target interactions, and prioritize candidates for experimental follow-up. However, predictive performance alone is insufficient when computational outputs are used to support therapeutic interpretation, mechanism hypotheses, safety reasoning, or candidate selection. This article proposes an explainable molecular AI framework for phytopharmacology that links chemical feature explanations, target-level interpretation, pathway-level reasoning, ADMET and toxicity interpretation, uncertainty assessment, expert review, and therapeutic decision confidence. The framework treats explainability as a structured decision-support process rather than as proof of mechanism, safety, or efficacy. At the chemical level, explanations may connect molecular descriptors, fingerprints, graph features, substructures, attention patterns, and counterfactual changes to predicted bioactivity. At the target level, interpretable models may support hypotheses about phytochemical–target interactions, target-family relevance, and binding plausibility, while requiring external validation. At the pathway level, network pharmacology and systems-level interpretation can organize predicted targets into biological hypotheses, but remain vulnerable to database bias, overconnected nodes, and weak causal specificity. Decision confidence is therefore framed as a convergence judgment across explanation quality, uncertainty, applicability domain, data provenance, pharmacological plausibility, ADMET concerns, and expert review. The main contribution is an integrated architecture for transparent candidate prioritization in natural product drug discovery. The framework can help researchers avoid overreliance on opaque predictions, visually persuasive explanations, isolated target claims, or unsupported pathway diagrams, while guiding reproducible reporting, validation planning, and future development of responsible explainable AI tools for phytopharmacology.