Molecular Docking of Natural Products: Critical Appraisal of Current Methodology and Practical Guidelines

Molecular docking is among the most widely used techniques in structure-based drug discovery and has become integral to natural-product research. Despite substantial advances in computational algorithms and artificial intelligence, the methodological quality of published docking studies on natural compounds remains highly variable, and generally accepted methodological guidelines have yet to be established. This review critically evaluates current approaches to molecular docking of natural compounds, examines the specific features of different natural-product classes and protein targets, and offers practical recommendations for the design and validation of docking studies. The review covers the main stages of molecular docking, contemporary search algorithms and scoring functions, protein and ligand preparation, the fundamental limitations of the method, strategies for result validation, and the emerging role of artificial intelligence in computational molecular modeling. A central component is a systematic methodological audit of publications within a prespecified 2025 coverage window, identified by Scopus and PubMed searches executed in August 2026. The audit assessed methodological reporting completeness only and was not intended as a systematic review of biological or pharmacological findings. The audit comprised GPT-assisted structured coding of 1127 publications, detailed full-text assessment of a random sample of 80 studies drawn entirely from the same corpus, and independent human validation of 20 randomly selected Stage 2 publications. In the 80-publication full-text sample, redocking was reported in 7.5% of publications, a qualifying redocking RMSD below 2 Å in 6.2%, positive controls in 87.5%, molecular dynamics in 41.2%, MM/PBSA or MM/GBSA in 12.5%, interaction analysis in 98.8%, and ADMET assessment in 35.0%. Agreement between Stage 1 and Stage 2 was 98.3% across 525 paired criterion decisions. Independent human assessment of the 20-publication validation subsample agreed with Stage 2 in 135 of 140 criterion decisions (96.4%); the five discrepancies all involved AI-coded indeterminate/non-confirmed labels (Unconfirmed or N/A) that the human reviewer classified as No. On the basis of these findings, we propose a practical workflow aimed at improving the reproducibility and methodological rigor of molecular docking studies of natural compounds. A complementary targeted case-enriched validation of redocking and redocking-RMSD classification included 19 publications and two human reviewers. The reviewers reached identical classifications in all 38 criterion decisions; their consensus agreed with Stage 2 in 31 of 38 decisions (81.6%), with seven revisions across four publications. Because this sample was deliberately enriched for informative and ambiguous cases, it was used to examine classification boundaries rather than to estimate prevalence. Because PubMed retrieval was restricted to free full text and the Scopus search used restricted bibliographic fields, these reporting-completeness and validation frequencies primarily characterize an accessibility-enriched corpus and may not fully generalize to subscription-only or otherwise less-accessible journals. By combining a critical appraisal of current methods with a quantitative assessment of published studies and practical recommendations for standardizing the molecular docking of natural compounds, this review offers guidance for researchers in computer-aided drug design.