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We present a data-driven workflow for drug repositioning that integrates high-throughput and virtual screening, molecular docking, and mechanistic pharmacokinetic modeling with biological validation using lung fibrosis as a model disease. High-throughput screening assays and LINCS L1000, SwissTarget, and Unified Knowledge Space (UKS) databases identified ouabain and helenalin as candidates targeting lung fibrosis-related genes. Pharmacophore modeling, docking analysis, and physiologically based pharmacokinetic modeling confirmed drug-like properties comparable to current lung fibrosis treatments. In silico findings were validated using RNA-sequencing data from idiopathic pulmonary fibrosis patients and quantitative polymerase chain reaction data from human alveolar epithelial cells exposed to profibrotic transforming growth factor-\u03b2. In\u00a0vitro, helenalin and ouabain induced distinct gene expression profiles. 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