The pairwise similarity calculations are performed with the Tanimoto coefficient . The data visualization techniques to obtain visual representation of the chemical space implemented in the current version of MAYA are principal component analysis (PCA) (26) and t-distributed stochastic neighbor embedding (t-SNE) through the scikit-learn library . MAYA includes an "activity index" as the sum of the activity values of each compound across the different biological endpoints. The activity index can be mapped on the visualizations of the chemical spaces using a continuous color scale as exemplified in the Results and Discussion.