Phytochemical Intelligence for Drug Discovery: Connecting Virtual Screening, Target Prediction, ADMET Profiling, and Mechanism-Based Candidate Prioritization

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Phytochemicals and plant-derived molecules remain central to drug discovery because they offer chemically diverse scaffolds, biological complexity, and mechanistic breadth that can complement synthetic libraries. However, the computational prioritization of phytochemicals is often fragmented across isolated docking studies, target-prediction outputs, network analyses, and ADMET filters. This article proposes “phytochemical intelligence” as a conceptual framework for integrating virtual screening, target prediction, ADMET profiling, mechanism-based filtering, and candidate prioritization into a transparent decision-support logic for early-stage drug discovery. The framework positions virtual screening as a hypothesis-generating layer for identifying candidate molecular interactions, target prediction as a mechanism-inference layer for linking compounds to plausible biological targets, and ADMET profiling as a developability-oriented filter for identifying pharmacokinetic and safety liabilities. Mechanism-based prioritization is then used to distinguish weak computational hits from candidates with convergent evidence across molecular fit, target plausibility, pathway relevance, safety risk, assay feasibility, and translational readiness. The framework emphasizes that computational evidence can support prioritization, but cannot establish bioactivity, safety, therapeutic efficacy, or clinical relevance without experimental validation. Its main contribution is a structured decision architecture that separates prediction, plausibility, validation, and translational readiness while enabling reproducible evidence integration. Practically, phytochemical intelligence can help natural product researchers, medicinal chemists, pharmacologists, and computational scientists select stronger candidates for experimental testing, avoid overreliance on single-method outputs, and design more coherent validation strategies. Future research should focus on transparent reporting, uncertainty assessment, benchmarked prediction models, experimentally confirmed mechanisms, and interdisciplinary workflows that connect computational triage with pharmacological and translational development.