N. Schaduangrat

Interpretable QSAR modelling for PPAR-γ agonist prediction by integrating a stacking strategy, docking, and MD simulations.

Meta-iPPAR is developed, an integrative in silico framework combining stacked machine learning, molecular docking, and molecular dynamics simulations for the identification of PPAR-γ agonists that will be an effective computational tool for screening and prioritizing potential compounds targeting PPAR-γ in the early stage of drug development pipelines.