Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) will modernize and transform clinical development because of data availability and the computational power that can efficiently handle this data . Specifically, studies using ML and quantitative modeling have been widely applied to clinical trials with varying goals and using diverse types of independent predictive variables. However, the annotation of clinical trial datasets is highly time-consuming and require domain expertise, due to which nearly all outcome prediction studies use commercially available, annotated clinical trial datasets . A labyrinth set of studies evaluate various factors that affect trial outcomes and span a wide set of questions (see for a detailed review of ML and non-ML research). Yamaguchi et al , apart from implementing a logistic regression model that uses target, mechanism of action (MOA), modality, and application of drugs as features to predict clinical trial success, also provide a review that classifies literature based on the type of features used to predict clinical outcomes. We summarize the principal differentiation in Section 1.1.1 and refer the reader to the two recent studies that provide exhaustive reviews of predictive algorithms.