We also estimated the important substructures of Fa using a conventional MPNN model with Monte Carlo tree search . For this calculation, we used two models to investigate the positive and negative substructures of Fa. To achieve this, an additional MPNN model with an objective variable of 100 Fa (%) was constructed based on the building process in the conventional MPNN model. For the Monte Carlo tree search, "Chemprop.interpret.py" was used in Python (ver. 3.7.10) from the Chemprop library (version 1.3.1), and the parameters were set to default . The positive substructures were clustered into 30 groups using FragFp in Data Warrior (version 5.2.1) . The centroid structure was selected as representative of each cluster. The compounds that included these substructures and whose Fa were > 95% were scrutinized. All negative substructures were scrutinized with compounds whose Fa were below 5%.