ADMET Prediction for Plant-Derived Molecules: A State-of-the-Art Review of In Silico Safety, Bioavailability, Toxicity, and Developability Models

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Plant-derived molecules remain central to pharmacological discovery because they occupy chemically diverse regions of bioactive chemical space, yet their development is frequently constrained by solubility, permeability, metabolic instability, off-target toxicity, formulation barriers, and uncertain translational performance. In silico ADMET prediction has therefore become an important early-stage strategy for prioritizing phytochemicals, natural products, secondary metabolites, and herbal bioactives before resource-intensive experimental evaluation. This state-of-the-art review examines current computational approaches for predicting safety, bioavailability, pharmacokinetic behavior, toxicity, and developability-relevant liabilities in plant-derived molecules. The review emphasizes molecular representation strategies, including descriptors, fingerprints, SMILES, molecular graphs, and learned embeddings, together with rule-based filters, QSAR, machine learning, deep learning, graph neural networks, multitask models, applicability-domain methods, interpretability tools, and uncertainty-aware prediction. Endpoint coverage is considered across absorption, solubility, permeability, oral bioavailability, distribution, plasma protein binding, blood–brain barrier penetration, CYP450 interaction, transporter effects, clearance, half-life, hepatotoxicity, cardiotoxicity, hERG liability, genotoxicity, mutagenicity, carcinogenicity, reproductive toxicity, skin sensitization, and acute toxicity. The synthesis highlights that computational ADMET models can support early triage, risk flagging, hypothesis generation, and prioritization, but their outputs should not be interpreted as confirmed safety, bioavailability, toxicity, or clinical developability. The main contribution is a developability-oriented framework that links plant-derived chemical diversity with endpoint-specific model selection, validation safeguards, experimental confirmation, and translational readiness. Future progress will depend on better natural-product datasets, scaffold-aware validation, uncertainty reporting, explainability, reproducibility, and integration with pharmacology, toxicology, formulation science, and medicinal chemistry.