Topological Distance-Based Electron Interaction Tensor to Apply a Convolutional Neural Network on Drug-like Compounds

Deep learning (DL)
models in quantitative structure–activity
relationship fed the molecular structure directly to the network without
using human-designed descriptors by representing molecule as a graph
or string (e.g., SMILES code). However, these two representations
were oversimplification of real molecules to reflect chemical properties
of molecular structures. Given that the choice of molecular representation
determines the architecture of the DL model to apply, a novel way
of molecular representation can open a way to apply diverse DL networks
developed and used in other fields. A topological distance-based electron
interaction (TDEi) tensor has been developed in this study inspired
by the quantum mechanical model of the molecule, which defines a molecule
with electrons and protons. In the TDEi tensor, the atomic orbital
(AO) of each atom is represented by an electron configuration (EC)
vector, which is a bit string based on the presence and absence of
electrons in each AO according to spin indicated by positive and negative
signs. Interactions between EC vectors were calculated based on the
topological distance between atoms in a molecule. As a molecular structure
was translated into 3D array, CNN models (modified VGGNet) were applied
using a TDEi tensor to predict four physicochemical properties of
drug-like compound datasets: MP (275,131), Lipop (4193), Esol (1127),
and Freesolv (639). Models achieved good prediction accuracy. PCA
showed that a stronger correlation was observed between the extracted
features and the target endpoint as features were extracted from the
deeper layer.