Similarly, large amounts of SD activity values are available to support the goal of multi-fidelity integration for improving the quality of confirmatory-level predictions. To further support the main modelling goal, we provide 5 random splits per dataset (80%/10%/10% split ratios for the train/validation/test sets) for the DR data. The accompanying source code allows easily converting the CSV files obtained previously into individual train/validation/test CSV files corresponding to each random split seed. A wide range of machine learning techniques are applicable on MF-PCBA, including any algorithm that operates on fixed-dimension vector representations (molecular fingerprints, physical and chemical descriptors, atom and bond encodings, etc.).