Gayvert et al. (2017) used the structures and properties of drugs and targets to predict drug toxicity based on a random-forest model . explored seven commonly used classifiers and found that kNN gives the highest AUCs (0.81) in predicting drug approvals . Seo et al. proposed an outer product-based convolutional neural network that employs the augmented outer product to combine chemical features of drugs and target-based features to predict the odds of clinical trial outcomes. Vidhya et al. employed a combination of biological activities, physicochemical properties, target-related features, and NLPbased compound representation to accurately forecast trial outcomes. This was achieved through the integration of Graph Database and Ensemble Learning techniques . Qi and Tang (2019) built a recurrent neural network model to predict phase III trial outcomes based on patient records from the previous phase II trial . Siah et al. (2020) predicted drug approvals using a statistical machine learning approach that considers both drug characteristics and trial characteristics . Abidi et al. (2020) developed a machine learning model that predicts clinical trial enrollment rates based on historical data and trial characteristics, allowing sponsors to optimize their recruitment strategies.