This article describes the development of a small-molecule inhibitor, INS018_055, targeting TRAF2- and NCK-interacting kinase (TNIK) for the treatment of idiopathic pulmonary fibrosis (IPF). Using a predictive artificial intelligence (AI) approach, the authors identified TNIK as an anti-fibrotic target. INS018_055, generated through AI-driven methodology, exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled, or topical administration. The compound also demonstrates anti-inflammatory effects, validated in multiple in vivo studies. Its safety, tolerability, and pharmacokinetics were assessed in two phase I clinical trials involving healthy participants, showing promising results. The work demonstrates the capabilities of the generative AI-driven drug-discovery pipeline, highlighting the potential of AI in streamlining drug design for fibrotic diseases. The discovery and development of INS018_055 from target identification to preclinical candidate nomination took approximately 18 months, showcasing the efficiency of the AI-driven approach.This article describes the development of a small-molecule inhibitor, INS018_055, targeting TRAF2- and NCK-interacting kinase (TNIK) for the treatment of idiopathic pulmonary fibrosis (IPF). Using a predictive artificial intelligence (AI) approach, the authors identified TNIK as an anti-fibrotic target. INS018_055, generated through AI-driven methodology, exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled, or topical administration. The compound also demonstrates anti-inflammatory effects, validated in multiple in vivo studies. Its safety, tolerability, and pharmacokinetics were assessed in two phase I clinical trials involving healthy participants, showing promising results. The work demonstrates the capabilities of the generative AI-driven drug-discovery pipeline, highlighting the potential of AI in streamlining drug design for fibrotic diseases. The discovery and development of INS018_055 from target identification to preclinical candidate nomination took approximately 18 months, showcasing the efficiency of the AI-driven approach.