We implemented two types of data splits: random split and scaffold split, both adapted from the DeepChem 51 library. The training set was partitioned into internal training (80%) and test (20%) sets. Parameter optimization was performed on these partitions in a five-fold crossvalidation format. For this purpose, the split was performed five times each for both split types. Finally, models based on the best parameters were built using the unpartitioned training set and evaluated on the prospective validation set and FDA approved drugs. All learning methods are briefly explained below.