In addition to its use in high-throughput virtual screening, diversity analysis, and virtual library design, it is also possible to use ChemPlot for visualizing the applicability domain of AI models. Here, the applicability domain refers to the region of chemical space where a model is expected to perform reliably and make confident predictions. For instance, when the predicted molecule is within the applicability domain, confidence can be put in the prediction, and when it is outside the applicability domain the prediction can be considered as dubious. By visualizing the chemical space of the training and test datasets of the models, the level of confidence of the prediction for new molecules can be determined based on their chemical space placement.