Data: With a total of 18.8 million data points, comprising 18 million inactive and 0.5 million active points, each has a target, after removing duplicated SMILES for all data the data become 1.2 million unique compounds, consisting of 300k active and 900K inactive compounds. Models trained on imbalanced data tend to have high specificity, but lower accuracy and sensitivity compared to those trained on balanced data. Models based on Morgan2 perform slightly better than MACCS on both imbalanced and balanced datasets. Hyperparameter optimization of balanced data led to a slight increase in all evaluation metrics, particularly when dealing with limited data. Most Random Forest models performed well on the balanced dataset using 16 estimators as the optimal number. Even with high evaluation metrics, the small size of the dataset make it difficult to determine whether the model would perform well in real-time scenarios. Further examples can be found in the supplementary materials for additional research and use. The Streamlit app provides the capability to utilize the model in both online and offline modes, generating target predictions as well as scripts that aid in identifying active targets against certain molecules, which can be easily downloaded. The app and models are provided with the hope of reducing the cost of cancer treatments by discovering selective kinase inhibitors or making it available for free.