We have shown that including the bioactivity of the drug against the putative targets common to the disease considered in the clinical trial in question provides enhanced predictive power to our Random forest classifier. Early-stage prediction of the outcome of clinical trials will help drug discovery researchers to better estimate the probability of success based on tangible activity measurements. Bioactivity, as represented by Stdval, has a significant effect on the model's ability to predict trial outcomes. Further, it is shown to provide a significant advantage in predicting trials of top therapeutic categories (ATC Groups -N, C, A), as described in the results. This emphasizes the importance of considering the mechanism of action data when estimating trial success through our or any similar models, researchers may wish to use. It is important to note that we have not used (due to lack of access) larger clinical trial datasets, but expect that it will lead to significantly better predictive power, especially for the clinical trial's 'Fail' category.