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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]