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Highlights from the article: After confirming the reasonable performance of our GCN model, we deleted each one from the 12 atom features and trained the feature-deleted models with the same protocol as before in order to understand the contribution of individual atom features to the aqueous solubility.

This work proposes the use of IN−A (IN: identity matrix; A: adjacency matrix), instead of IN+A, the normalized form of which has intensively been used for the construction of graph convolutional networks (GCNs), in deep‐learning chemistry. The performance of the GCN model with D−1/2IN−AD−1/2 in its convolution step is at least on a par with the vanilla GCN that uses D~−1/2IN+AD~−1/2 (D~: degree matrix of IN+A) in various chemistry datasets, such as FreeSolv, ESOL, lipophilicity, and blood–brain barrier penetration datasets. It could be seen that the use of IN−A might be more chemically intuitive than the use of IN+A, potentially embracing the information on bond properties, such as dipole moment, and functional groups in a molecule. This work suggests unavoidable necessity of tackling molecular‐representation problems in deep‐learning chemistry from unprecedented angles of view for advanced development and construction of chemically intuitive deep‐learning models. [ABSTRACT FROM AUTHOR]

3D graph convolutional network (3DGCN) is a graph neural network (GNN) variant that utilizes the 3D bond information on molecules for chemical tasks. In 3DGCN, the pooling operation extracts the molecular features from the atomic features for molecule embedding and dimensionality reduction. In this work, we investigated the pooling effects on the performance of 3DGCN in the classification of protein–ligand binding events, especially when a ligand is rotated with respect to a target protein in the 3D Cartesian coordinate. 3DGCN showed the general ability for recognizing the ligand rotation, and its prediction accuracy was found to be pooling‐dependent. For example, 3DGCN with max pooling did not recognize the rotations of known active ligands for human β‐secretase 1 faithfully, compared with the other pooling operations (sum, avg, and set2set). This work would contribute to augmented architecture evolution of 3DGCN for the chemical tasks that require the 3D molecular information including chirality. [ABSTRACT FROM AUTHOR]