Frequently used organic solvents in perovskite synthesis are categorized into two subclasses (agonist and binding) of active/ inactive endocrine disruptors (EDs). These two subclasses denote a molecule's ability to interact with the estrogen receptor . For a chemical, the state of being active or inactive in one of the subclasses is independent of its nature in the other subclass. However, if the chemical is "Active" in any of the subclasses, then it's potentially an EDC. This classification is done with the help of a deep-learning model that takes SMILES as the inputs and gives a multi-output binary classification. The studies that back up our data for this classification are mentioned in the last column. In this section we address the ambiguity associated with the concerned deep learning model to underpin its decision making and filter out possible spurious predictions. A post-prediction uncertainty analysis has been performed in this section to assess the precision of the data-driven model by quantifying the uncertainty associated with the predictions. Uncertainty in prediction can arise from different sources. The ML model needs to be representative for organic molecules in general to obtain an interpretable prediction in order to accurately classify a solvent as either active or inactive. This capacity of the model depends on the data used for training. In our current work we are using the data-driven model that is trained on the list of EDCs from the US EPA and make a prediction on the list of chemicals extracted from the literature on perovskites. Data from different sources not necessarily may follow the same probability distribution. Furthermore, there are aleatory uncertainties since we are relying on statistical methods to obtain the list of chemicals used for perovskite synthesis. Thus, there is a need to acknowledge the problem of data belonging to a different distribution using relevant UQ method. The UQ converts the point prediction into probabilistic prediction to gain more confidence in our prediction.