However, it should be noted we did not optimize hyperparameters for these models and just used them with their defaults. Therefore, it is possible a better performance could still be achieved for XGBRegressor as well as ChemProp. Again, we observed that the clustered split showed degraded performance across all data sets and the variance between replicas was larger (compare Figure and). This is consistent with Experiment 1. The XGBRegressor also showed larger variance in predictions as compared to ChemProp, which might indicate that ChemProp is more stable and consistent in its predictive performance. These case studies were not by all means complete or exhaustive, but they illustrate how QSPRpred could be used to run various experiments for validation of novel QSPR methodologies on a variety of problems. Moreover, the available code in the qspbench repository can be quickly derived from to create other benchmarks. We think that this is an especially interesting prospect for developers of new models who can validate their approach within QSPRpred, but the simple act of integrating it into the benchmarking workflow also makes it readily available for deployment by others.