Quality of prediction for toxicity assessment has been measured in recent research using variability in ensemble learning and latent space distance metric . Under these methods, the uncertainty associated with each data point is computed with respect to other training samples, and thus do not take into account the discrepancy that may arise due to data from different sources. We have adopted a Shannon entropy based UQ method to quantify the uncertainty in prediction that may arise due to different sources of data and is agnostic to the type of model architecture. Shannon entropy is a well-known measure of randomness or uncertainty and finds its application in classification, parameter learning, and active learning .