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Rotational Variance-Based Data Augmentation in 3D Graph Convolutional Network
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DOI：10.1002/asia.202100789.png)
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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 beta-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.
Keyword:
Data augmentation
Deep learning
3D Graph convolutional network
Protein-ligand binding
Rotational variance
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Enhanced Deep-Learning Prediction of Molecular Properties via Augmentation of Bond Topology.png)
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OAAI分享收藏Layer-wise relevance propagation of InteractionNet explains protein-ligand interactions at the atom levelInteractionNet的逐层相关性传播解释了原子水平上的蛋白质-配体相互作用SCIENTIFIC REPORTSOAAI
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