Addendum: Data splitting against information leakage with DataSAIL

In our recently published manuscript, we presented the DataSAIL tool for automatically splitting datasets used in the training of machine learning models while minimizing information leakage between the splits (e.g., training, validation, and test sets).In evaluating our method, we utilized several benchmark datasets and compared DataSAIL to existing tools and splitting algorithms.While the publication focused on comparing automated tools for data splitting, many of the datasets used in our study have been published together with corresponding data splits created by the authors.In this addendum, we extend our analysis to comparing DataSAIL splits to these predefined data splits.We achieve this by measuring the amount of leaked information between splits using the scaled version of the leakage score L(), as defined in Eqs.(2) and (20) of our original article.Apart from the three datasets used in our paper-MoleculeNet 1 , LP-PDBBind 2 , and PLINDER 3 -we also extend our analysis to PINDER 4 and the human gold-standard dataset for protein-protein interaction (PPI) prediction by Bernett et al. 5 .This is done for completeness and due to their popularity in the field of PPI prediction. MoleculeNetThe authors of MoleculeNet provide recommended splits for each dataset.In Table 1, they are computed using the DeepChem package 6 and primarily comprise a random data split.Exceptions include QM7, which features a stratified split, and HIV, BACE, and BBBP, which have scaffold splits.Each dataset is split into 80% training data, 10% validation data, and 10% test data.In our original work, we utilized DataSAIL's similarity-based splitting to achieve an 80:20 separation between the training and test data.Therefore, we had to redo the data splitting, leading to slightly different leakage measurements compared with Table 1 in the original publication.In all cases, DataSAIL splits offer reduced data leakage.