This section explains the machine learning models built using the data extracted from the integrated data source, explained in Section 2.2. All the models are implemented in Python 3.6 with Sci-kit learn and its dependent packages . The steps in the pipeline are shown in Figure . We begin with an exploratory analysis of the data and filtering of a meaningful subset as explained in Section 2.5. Next, we experiment with various representations of the features, particularly, various compound fingerprints detailed in Section 2.4. Next, we combine the best-performing fingerprints with compound-physiochemical features. We build models with and without the inclusion of bioactivity features to evaluate their utility. After experimenting with the combination of features, we compare the performance of multiple models towards predicting the outcome of clinical trials. Performance evaluation is done based on the most meaningful traintest split in the context of class imbalance and to tune the parameters on the training set for each of the models considered. The rest of this section details the rationale and nuances of the model construction process.