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This work proposes the data augmentation by molecular rotation, with consideration that the protein‐ligand binding events are rotation‐variant. As a proof‐of‐concept, known active (i. e., 1‐labeled) ligands to human β‐secretase 1 (BACE‐1) are rotated for the generation of 0‐labeled data, and the rotation‐dependent prediction accuracy of 3D graph convolutional network (3DGCN) is investigated after data augmentation. The data augmentation makes the orientation‐recognizing ability of 3DGCN improved significantly in the classification task for BACE‐1/ligand binding. Furthermore, the data‐augmented 3DGCN has a capability for predicting active ligands from a candidate dataset, via improved performance of orientation recognition, which would be applied to virtual drug screening and discovery. [ABSTRACT FROM AUTHOR]

Front Cover: Rotational Variance-Based Data Augmentation in 3D Graph Convolutional Network (Chem. Keywords: Data augmentation; Deep learning; 3D Graph convolutional network; Protein-ligand binding; Rotational variance EN Data augmentation Deep learning 3D Graph convolutional network Protein-ligand binding Rotational variance 2568 2568 1 09/22/21 20210920 NES 210920 B Data augmentation for 3D graph convolutional network (3DGCN) b has been achieved, for the classification task of human -secretase 1 (BACE-1)-ligand binding, by randomly rotating known active (1-labeled) ligands in the 3D Cartesian coordinate and assigning the rotated ones as label-0. Data augmentation, Deep learning, 3D Graph convolutional network, Protein-ligand binding, Rotational variance. [Extracted from the article]