Ensemble Geometric Deep Learning of Aqueous Solubility

Geometric deep learning is one of the main workhorses
for harnessing
the power of big data to predict molecular properties such as aqueous
solubility, which is key to the pharmacokinetic improvement of drug
candidates. Two ensembles of graph neural network architectures were
built, one based on spectral convolution and the other on spatial
convolution. The pretrained models, denoted respectively as SolNet-GCN
and SolNet-GAT, significantly outperformed the existing neural networks
benchmarked on a validation set of 207 molecules. The SolNet-GCN model
demonstrated the best performance on both the training and validation
sets, with RMSE values of 0.53 and 0.72 log molar unit and Pearson r2 values of 0.95 and 0.75, respectively. Further,
the ranking power of the SolNet models agreed well with a QM-based
thermodynamic cycle approach at the PBE-vdW level of theory on a series
of benzophenylurea derivatives and a series of benzodiazepine derivatives.
Nevertheless, testing the resultant models on a set of inhibitors
of the macrophage migration inhibitory factor (MIF) illustrated that
the inclusion of atomic attributes to discriminate atoms with a higher
tendency to form intermolecular hydrogen bonds in the crystalline
state and to identify planar or nonplanar substructures can be beneficial
for the prediction of aqueous solubility.