In silico design of potent inhibitors targeting TNF-alpha, IL-1β, and TGF-β of inflammatory disease using QSAR, docking, MD simulation, ADMET, and DFT studies

Inflammatory diseases, such as rheumatoid arthritis, pulmonary fibrosis, and autoimmune disorders, remain a challenge to treat with current therapeutics due to limited efficacy and adverse side effects. Targeting key mediators such as tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and transforming growth factor-beta (TGF-β) offers a promising therapeutic strategy. Geniposide-derived compounds were designed as potential inhibitors of TNF-α, IL-1β, and TGF-β using advanced computational approaches, including QSAR modeling, Molecular docking, and molecular dynamics (MD) simulations, with ADMET profiling, and density functional theory (DFT) analysis to assess drug-likeness and electronic properties for molecular stability. Among the newly designed analogues, compound AIF3 demonstrated superior binding affinity to IL-1β and TGF-β compared with the lead compound and standard drugs. Structural modifications significantly improved the predicted biological activity of the newly designed analogues while QSAR analysis confirmed strong predictive reliability. MD simulations validated the stability of AIF3 within IL-1β and TGF-β binding pockets, ADMET and DFT analyses supported favourable pharmacokinetic and electronic characteristics, further reinforcing its potential. This study highlights the potential of the AIF3 scaffolds derived from geniposide-based derivatives as novel anti-inflammatory agents with enhanced inhibitory potential against IL-1β and TGF-β, coupled with favourable drug-like properties. These findings underscore the promise of computational drug design in developing more effective therapeutics to overcome current limitations in the management of inflammatory diseases. • Geniposide-derived compounds were designed to target TNF-α, IL-1β, and TGF-β. • QSAR modeling showed high predictive reliability for the designed compounds. • Electron-donating modifications enhanced predicted anti-inflammatory activity. • AIF3 showed strong binding and stable interactions with IL-1β and TGF-β. • ADME and DFT analyses supported good drug-likeness and molecular stability.