Dataset for:Exploring the Pharmacological Properties and Mechanism of Action of Lithocarpus litseifolius (Hance) Chun. in Treating Diabetic Neuropathy Based on SwissADME, Network Pharmacology, and Molecular Docking

This dataset contains all raw and processed files that support the manuscript investigating the anti-diabetic-neuropathy (DN) potential of the medicinal plant Lithocarpus litseifolius. The study integrates SwissADME profiling, network-pharmacology target mining and molecular-docking validation to identify drug-like constituents, core molecular targets (AKT1, BCL2, EGFR, ERBB2, ESR1, HSP90AA1, SRC, TP53) and relevant signalling pathways (PI3K-Akt, MAPK, AGE-RAGE, etc.).Content summaryCompounds/– SMILES, SDF and MOL2 files for the 45 finally analysed constituents (32 flavonoids, 5 phenolics, 8 triterpenoids).– SwissADME output table (Excel) reporting Lipinski violations, ESOL solubility, GI absorption, BA %, P-gp substrate status, PAINS/Brenk alerts.Targets/– 1,346 unique SwissTargetPrediction hits (Homo sapiens, probability > 0) for all compounds.– 7,745 non-redundant DN-related targets extracted (DisGeNet, GeneCards, OMIM).– 246 intersecting “compound–disease” targets with Entrez IDs and official gene symbols.Networks/– STRING PPI (confidence ≥ 0.7) in TSV and Cytoscape (.cys) formats; node degree and 6-centrality metrics included.– “Drug–compound–target” network (.cys & .graphml) showing 290 nodes / 1,163 edges.Enrichment/– GO results (BP, CC, MF) and KEGG pathways (p ≤ 0.05, FDR q ≤ 0.05) as separate CSV files plus R-plots (png/pdf).Docking/– PDB codes of 8 core proteins, original co-crystal ligands, and CB-Dock2 parameter files (config.txt, box centre/size).– RMSD validation log (≤ 2 Å).– Binding-energy tables (CSV) for:– 17 core-compound vs core-target pairs– 40 “key-compound” vs core-target pairs– Lowest-energy poses (-5 to -9.4 kJ mol⁻¹) in MOL2 format and 3-D interaction images (png).– Positive-control data (approved drugs) for each target.Summary tables/– Master Excel file listing compound codes, names, molecular formula, MW, HBA, HBD, XLogP3, ESOL, GI, log Kp, P-gp, NRV, BA, PAINS/Brenk flags, highest Degree in network, docking scores vs each core target.Software & codeR 4.4.2 scripts for GO/KEGG (clusterProfiler, enrichplot, ggplot2) and Python notebooks for data cleaning are provided in the Code/ folder.Reuse potentialThe dataset enables re-analysis with updated target databases, comparison with other anti-DN agents, or development of multi-target QSAR/machine-learning models. All small-molecule and protein structures are ready for molecular-dynamics or free-energy perturbation studies.