Quantification of the uncertainty in the QSPR model predictions will be important in the broader relevance of the framework. While performance metrics on the test set and the bootstrapping analysis can estimate the average prediction error, quantifying the uncertainty on individual predictions would be highly desirable. This may be achieved through conformal prediction. Another advantage of conformal prediction in this generative drug design framework could be to create an active learning loop, by identifying compounds to synthesize and test to further expand the applicability domain of the QSPR models.