AI-driven target discovery and clinical development of candidate drugs for idiopathic pulmonary fibrosis: A review

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease with high mortality and limited treatment options, urgently requiring new therapeutic targets and drug development strategies. In recent years, the rapid advancement of artificial intelligence (AI) technology in the biomedical field has provided robust support for target discovery and candidate drug design in IPF. Furthermore, AI-assisted drug design facilitates the selection and optimization of novel candidate drugs. Against this backdrop, "rentosertib," one of the pioneering fully AI-driven drug candidates to enter clinical trials, has demonstrated promising efficacy and safety, suggesting potential for clinical translation. This narrative review examines AI-platform-driven IPF target discovery methods, clinical development progress of candidate drugs, and existing issues. It outlines future directions for AI technology in precision IPF therapy, aiming to provide theoretical foundations and practical guidance for innovative drug development in this field.