(5) Coverage: Calculate the percentage of molecules in A with a similarity greater than γ to those in B. (6) Novelty: Calculate the percentage of molecules in B with a similarity smaller than γ to those in A. (7) Vary the threshold γ from 0 to 1, and draw a curve representing the balance between coverage and novelty (CN curve), as in Figure . The CN curve shows an upward or downward convex pattern depending on the inclusive relationship of the distributions of A and B. Ideally, a set of reasonably novel and diverse molecules should be generated, while maintaining a high coverage to existing molecules. This corresponds to a situation in which the distribution of B encompasses A. In such a case, the CN curve deviates slightly from the 45 • line and shows an upward convex pattern. For this criterion, SMC-RECUR-GTM, SMC-RECUR-GTM-SR-PL, and Random-RECUR showed better properties than the others (Figure ). When the threshold of Tanimoto similarity was set to γ ≥ 0.7, the coverage and novelty of SMC-RECUR-GTM and SMC-RECUR-GTM-SR-PL were both approximately 0.3 and 0.8, respectively. The analyses presented here can be performed using the Python package Seq-Stack-Reaction, which we have made available online under the license BSD 3-Clause . Seq-Stack-Reaction can be installed with Conda . Users can plug-in any model for property and synthetic reaction predictions. The list of commercial compounds and the maximum number of reaction steps can also be specified arbitrarily. In a parallel computing environment, the asynchronous parallel search algorithm can be executed by specifying an option. Additionally, a visualization of the predicted synthetic pathway network of the excavated products is implemented. Figure shows some examples of the visualization for the four identified products and their synthetic pathways. In Supporting Information (Additional file 1: Sec- tion S1), 10 predicted products and their synthetic pathway networks are shown as examples.