The public dataset utilized in this study was derived from ExCAPE-DB. All targets are wellstudied and include more than 1,000 bioactivity data points with pXC50 values. The simplified molecular-input line-entry system (SMILES) strings were obtained from ExCAPE-DB for all molecules, and canonicalized using the RDKit (version 2020.09.01) component "RDKit Canon SMILES" and "Speedy SMILES De-salt" in the KNIME (version 4.3.4). However, this public database contains no 'project registration date' and the compounds deposited in the underlying databases (ChEMBL and PubChem ) are usually done by publication or grouped upload, not reflecting realistic project time series optimization. Therefore, in order to mimic the time series of a practical drug discovery process that increases the activity with time elapsed, data was mapped onto a 'pseudo-time axis' as follows. We transformed the data by principle component analysis (PCA) using Datawarrior (ver 5.2.1) , and then the following three steps. The canonical SMILES of the public dataset were input to calculate the FragFp fingerprints. The FragFP fingerprints were used to calculate the normalized PCA components of 3 components. (2) Then, these components alongside pXC50 value of each compound (in total 4 dimensions) was used to obtain another PCA score of 3 components. These 3 final PCA components hence include information on both similarity of compounds in fingerprint space as well as bioactivity. (3) Finally, the Euclidean distance of all compounds in each dataset to the compound that has lowest pXC50 value was calculated using the final 3 PCA components. This process introduces an ordering of compounds in bioactivity space (from low to high potency), as well as chemical space (from a low potency starting point, to high potency compounds with increasing dissimilarity to the starting point). We are aware that this process does not necessarily resemble a real-world drug discovery project, but it at least represents compound progression towards higher-potency compounds, which, given the limited availability of public domain timestamped project data is the only practically feasible option we were able to identify for a public dataset. Then, the datasets were divided by both the activities and pseudo-stages.