In this paper, the author demonstrate the feasibility of developing a machine learning model for each kinase. By providing a user-friendly interface app that is ready to use, with the aim to make the process much easier for researchers to discover and utilize these models. The ability to predict active compounds for 480 targets represents a significant first step towards realizing KLIFSAI's dream of generating active compounds, predicting bioactivity, and performing kinase docking prediction. However, further activity prediction, optimization, and experimental validation will be necessary to advance this process. The way may be long, but small steps can make it farther possible, and the dreams of yesterday will become the realities of tomorrow.