Predictions were made using two CatBoost:CDK with TP models-one trained on the full dataset and another on the drug-like subset (Figure ). Their respective RMSE values were 0.3 and 0.2 lg(y 2 ). As expected, this shows that the model specialized in pharmaceuticals outperformed the one trained on larger, more diverse dataset. The latter model had to generalize across a broad spectrum of data, which may have reduced its accuracy for drugrelated predictions.