To deal with the low-data regime (n=3964, see 2.1.1) and the added model complexity of an MPNN (see 2.2.1), a transferlearning approach was adopted that uses a pre-training regime to learn molecular encodings on a larger dataset with a cheaply computed label. In this way, the RBFENN can be pre-trained on a domain of n=2550 2 -2550 ≈ 6.5 6 points (i.e. composed of all possible pairs of molecules in RBFE-Space); as a cheaply computed label the difference in estimated solubility (∆ESOL) was used. This property was chosen not because it is necessarily meaningful to this work's purposes, but because it is a complex descriptor that pushes the RBFENN to learn a more complete understanding of chemical structure a priori; similar approaches have been reported. Early-stopping patience for this phase was set to 5 epochs as early convergence (70-100 epochs) was observed.