Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network

Yang, Ziduo; Zhong, Weihe; Lv, Qiujie; Chen, Calvin Yu-Chian

doi:10.1039/d2sc02023h

Cited by 48 publications

(34 citation statements)

References 51 publications

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“…Therefore, we use U (cov) and U (ncov) to convert m i (cov) and m i (ncov) into the same scale before integrating them, as shown in eq , which avoids the issue described by Figure (a). Note that the noncovalent interactions within the ligand (or the protein) are also included because the receptive field of GNNs can be expanded with increasing layers , (i.e., an atom can access its k -hop neighbors for a k -layer GNN).…”

Section: Geometric Interaction Graph Neural Networkmentioning

confidence: 99%

“…GNNs cannot be fully trusted without understanding and verifying their working mechanisms, , which limits their application in drug discovery scenarios. In this section, we conduct two visual explanation experiments to rationalize GIGN.…”

Section: Visual Explanations For Gignmentioning

confidence: 99%

“…However, increasing T would also cause the oversmoothing problem. 43 Therefore, applying three heterogeneous layers for GIGN is already enough to capture sufficient structural information. We analyze the effect of m by setting it to different values (32, 64, 128, 192, 256, and 320).…”

Section: ■ Parameter Sensitivity Analysismentioning

confidence: 99%

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Geometric Interaction Graph Neural Network for Predicting Protein–Ligand Binding Affinities from 3D Structures (GIGN)

Yang

Zhong

et al. 2023

J. Phys. Chem. Lett.

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Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery. Recent advances have shown great potential in applying machine learning (ML) for PLA prediction. However, most of them omit the 3D structures of complexes and physical interactions between proteins and ligands, which are considered essential to understanding the binding mechanism. This paper proposes a geometric interaction graph neural network (GIGN) that incorporates 3D structures and physical interactions for predicting protein–ligand binding affinities. Specifically, we design a heterogeneous interaction layer that unifies covalent and noncovalent interactions into the message passing phase to learn node representations more effectively. The heterogeneous interaction layer also follows fundamental biological laws, including invariance to translations and rotations of the complexes, thus avoiding expensive data augmentation strategies. GIGN achieves state-of-the-art performance on three external test sets. Moreover, by visualizing learned representations of protein–ligand complexes, we show that the predictions of GIGN are biologically meaningful.

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Section: Geometric Interaction Graph Neural Networkmentioning

confidence: 99%

Section: Visual Explanations For Gignmentioning

confidence: 99%

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Geometric Interaction Graph Neural Network for Predicting Protein–Ligand Binding Affinities from 3D Structures (GIGN)

Yang

Zhong

et al. 2023

J. Phys. Chem. Lett.

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“…SA-DDI [ 44 ]: a GNN that used a message-passing neural network and a substructure-substructure interaction module to learn thorough and useful features. SA-DDI extracted features with message passing step T = 10 for DDIs prediction.…”

Section: Resultsmentioning

confidence: 99%

A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions

Lei

2023

PLoS Comput Biol

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Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been put forward using graph neural networks (GNNs) to forecast DDIs and learn molecular representations. However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two-dimensional molecular graph structure, while the interaction information between chemical substructure remains rarely explored, and it is neglected to identify key substructures that contribute significantly to the DDIs prediction. Therefore, we proposed a dual graph neural network named DGNN-DDI to learn drug molecular features by using molecular structure and interactions. Specifically, we first designed a directed message passing neural network with substructure attention mechanism (SA-DMPNN) to adaptively extract substructures. Second, in order to improve the final features, we separated the drug-drug interactions into pairwise interactions between each drug’s unique substructures. Then, the features are adopted to predict interaction probability of a DDI tuple. We evaluated DGNN–DDI on real-world dataset. Compared to state-of-the-art methods, the model improved DDIs prediction performance. We also conducted case study on existing drugs aiming to predict drug combinations that may be effective for the novel coronavirus disease 2019 (COVID-19). Moreover, the visual interpretation results proved that the DGNN-DDI was sensitive to the structure information of drugs and able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method enhanced the performance and interpretation capability of DDI prediction modeling.

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“…Feng et al proposed deepMDDI, which consists of an encoder by deep relational graph convolutional networks constraining with similarity regularization to capture the topological features of DDI network and a tensor-like decoder for multi-label prediction of DDI types [ 24 ]. Yang et al proposed a substructure-aware graph neural network, utilizing a message-passing neural network with a novel substructure attention mechanism and a substructure-substructure interaction module for DDI prediction [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

Lin

Chen

et al. 2022

J Cheminform

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The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid these accidental events, but also elaborate the underlying mechanisms by how DDIs occur. Several computational methods have been proposed for multi-type DDI prediction, but room remains for improvement in prediction performance. In this study, we propose a supervised contrastive learning based method, MDDI-SCL, implemented by three-level loss functions, to predict multi-type DDIs. MDDI-SCL is mainly composed of three modules: drug feature encoder and mean squared error loss module, drug latent feature fusion and supervised contrastive loss module, multi-type DDI prediction and classification loss module. The drug feature encoder and mean squared error loss module uses self-attention mechanism and autoencoder to learn drug-level latent features. The drug latent feature fusion and supervised contrastive loss module uses multi-scale feature fusion to learn drug pair-level latent features. The prediction and classification loss module predicts DDI types of each drug pair. We evaluate MDDI-SCL on three different tasks of two datasets. Experimental results demonstrate that MDDI-SCL achieves better or comparable performance as the state-of-the-art methods. Furthermore, the effectiveness of supervised contrastive learning is validated by ablation experiment, and the feasibility of MDDI-SCL is supported by case studies. The source codes are available at https://github.com/ShenggengLin/MDDI-SCL.

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Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network

Abstract: SA-DDI is designed to learn size-adaptive molecular substructures for drug–drug interaction prediction and can provide explanations that are consistent with pharmacologists.

Cited by 48 publications

References 51 publications

Geometric Interaction Graph Neural Network for Predicting Protein–Ligand Binding Affinities from 3D Structures (GIGN)

Geometric Interaction Graph Neural Network for Predicting Protein–Ligand Binding Affinities from 3D Structures (GIGN)

A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

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