Idiopathic Pulmonary fibrosis (IPF) is a terminal chronic lung disease causing lung scarring and a progressive decline in lung function. Current medications for this disease are minimal (Pirfenidone and Nintedanib). Emerging single-cell sequencing technologies can track the cellular dynamics in IPF progression and thus provide unrivaled opportunities to identify more effective therapeutic targets and drugs. In this paper, we have profiled the cellular states across different IPF stages using single-nuclei RNA-seq. Furthermore, we have developed a unified and computationally efficient drug repurposing framework called UNAGI (computational approach driven repurposed drugs for idiopathic pulmonary fibrosis), which reconstructs the cellular dynamics from the IPF single-nuclei RNA-seq data and identifies candidate drugs for the disease. UNAGI employs a deep generative adversarial variational-autoencoder with graph embedding to iteratively learn cellular dynamic graphs of IPF progressions and suggest a list of potential therapeutic targets from the reconstructed gene regulatory network that modulate the disease progression. UNAGI empowers in-silico explorations of intervention strategies to restore the healthy status of dynamic cell populations during the disease progression, which presents a short list of target pathways, potential repurposed drugs, and novel compounds against IPF. The UNAGI platform successfully identifies Nintadanib as an efficacious IPF drug and identifies several other potential compounds previously reported to repress induced pulmonary fibrosis. We have also systematically examined the top pathways identified by the model, which are significantly associated with pulmonary fibrosis as documented in the existing literature. These all manifest the effectiveness of the UNAGI platform.