Dataset for: Multi-scale computational toxicology of e-cigarette flavoring chemicals – network toxicology, AlphaFold 3, molecular dynamics, Mendelian randomization, and single-cell transcriptomics

This dataset contains the analysis code (Python and R scripts) and intermediate data files supporting a computational toxicology study of eight e-cigarette flavoring chemicals and their associations with chronic inflammatory diseases of the human digestive system. The repository includes: (1) network toxicology target prediction and disease gene collection pipelines; (2) protein-protein interaction analysis and hub gene identification; (3) Gene Ontology and KEGG pathway enrichment results; (4) AlphaFold 3 molecular docking input/output and scoring summaries; (5) molecular dynamics simulation metrics and MM-PBSA binding free energy calculations; (6) Mendelian randomization analysis scripts using eQTLGen and FinnGen R12 data; (7) GEO dataset validation and immune infiltration analysis code; (8) single-cell RNA-seq re-analysis scripts. Raw sequencing data were obtained from public repositories (GEO, eQTLGen, FinnGen) and are not redistributed here.