We use the aqueous solubility dataset from Sorkun et al. . This data is curated by merging and standardizing a total of nine different aqueous solubility datasets, with a total of 9982 data points. AqSolDB consists of SMILES representation of the molecules, experimental aqueous solubility reported in LogS, molecular weight (MolWt), octanol-water partition coefficient (MolLogP), molar refractivity (MolMR), number of non-hydrogen atoms (HeavyAtom-Count), number of hydrogen acceptors (NumHAcceptors), number of hydrogen donors (NumH-Donors), number of atoms that are not carbon or hydrogen (NumHeteroatoms), number of rotat-able bonds (NumRotatableBonds), number of valence electrons (NumValenceElectrons), number of aromatic rings (NumAromaticRings), number of saturated rings (NumSaturatedRings), number of aliphatic rings (NumAliphaticRings), number of total rings (RingCount), topological polar surface area (TPSA), Labute's approximation surface area (LabuteASA) ], Balaban's J index which is a graph index and a topological complexity index of the compound and BertZ CT(BertzCT) Bertz . To associate graphs capturing the connectivity between atoms and embed the chemical features in the respective graphs, we have used RDKit to extract the associated molecular representation from SMILES. We form the graphs treating the atoms and associated bonds as nodes and edges, respectively. Subsequently, the edges of the above graphs are weighted by the pairwise distances among the atoms. Next, we extract 13 local graph attributes at each node and compute feature-specific averages over all the nodes. Then, we append these 13 features with other chemical features in the final dataset for each molecule. The unique graph identifiers used are global efficiency (ge) of a graph , global clustering coefficient (out clcoef), betweenness centrality (bwn), node importance metrics (imp), leverage centrality (lev), s core decomposition of a network (score), global average shortest path length (PL aspl), path length diameter of the graph (PL d), participation coefficient of each vertex in the graph based on community membership (ptn), graph stability (stbl) average strength values for each node in the network (stg), average degree values for each adjacency matrix (degree) and eigenvectors (evec) of the adjacency matrix. Our complete data set includes 30 features combining all chemical and graphical characteristics of the molecules. The graph features are computed using the R packages NetworkToolbox and igraph .