A feature set with bioactivity and, primarily, drug-based variables (see Table for the full list) ensures that our model is not just a statistical retrospective study, but can be used in the early stages of drug discovery. This provides a probability value at the very early stages of drug discovery, and the opportunity to re-focus the prioritization of hits. Since the bioactivity is specific to a drug-target pair, unlike any other studies in the past, researchers will have opportunities to modify drug design protocols or prioritize existing drug candidates based on the candidates' probability to succeed in clinical trials. As far as we know, this is the only study that correlates bioactivity to clinical trial success. Further, we have exclusively chosen opensource data sources to produce our data, and provide access to all the code used to generate our dataset and produce the ML models in an unrestricted open-source format. This ensures that other groups with access to larger (commercial) annotated clinical trial data can use our algorithms, and possibly, achieve higher predictive scores.