This is a repository to help all readers who are interested in DDIs prediction. If you find there are other resources with this topic missing, feel free to let us know via github issues, pull requests or email: xzeng@foxmail.com. We will update this repository and paper on a regular basis to maintain up-to-date.
KEGG https://www.genome.jp/kegg/drug/
DrugBank https://go.drugbank.com/
SIDER http://sideeffects.embl.de/
TWOSIDES https://tatonettilab.org/resources/tatonetti-stm.html
OFFSIDES https://tatonettilab.org/resources/tatonetti-stm.html
BIOSNAP http://snap.stanford.edu/biodata/
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[2017]Computational prediction of drug-drug interactions based on drugs functional similarities
Ferdousi R, Safdari R, Omidi Y
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[2018]Deep learning improves prediction of drug– drug and drug–food interactions
Ryu JY, Kim HU, Lee SY
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[2018]Detection of drug–drug interactions through data mining studies using clinical sources, scientific literature and social media
Vilar S, Friedman C, Hripcsak G
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[2019]Deep learning for high-order drug-drug interaction prediction
Chen Y, Ma T, Yang X, et al
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[2019]Mlrda: A multi-task semi-supervised learning framework for drug-drug interaction prediction
Chu X, Lin Y, Wang Y, et al
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[2019]Kmr: knowledge-oriented medicine representation learning for drug–drug interaction and similarity computation
Shen Y, Yuan K, Yang M, et al
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[2020]A multimodal deep learning framework for predicting drug–drug interaction events
Deng Y, Xu X, Qiu Y, et al
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[2021]Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
Feixiong Cheng, Zhongming Zhao
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[2021]Moltrans: Molecular interaction transformer for drug–target interaction prediction
Huang K, Xiao C, Glass LM, et al
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[2022]Deside-ddi: interpretable prediction of drug-drug interactions using drug-induced gene expressions
Deng Y, Xu X, Qiu Y, et al
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[2022]Mddi-scl: predicting multi-type drug-drug interactions via supervised contrastive learning
Lin S, Chen W, Chen G, et al
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[2019]Mr-gnn: Multi-resolution and dual graph neural network for predicting structured entity interactions
Xu N, Wang P, Chen L, et al
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[2019]Drug-drug adverse effect prediction with graph co-attention
Deac A, Huang YH, Veliˇckovi´c P, et al
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[2020]Structure-based drug-drug interaction detection via expressive graph convolutional networks and deep sets (student abstract)
Sun M, Wang F, Elemento O, et al
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[2022]Multi-type feature fusion based on graph neural network for drug-drug interaction prediction
He C, Liu Y, Li H, et al
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[2022]Molormer: a lightweight self-attentionbased method focused on spatial structure of molecular graph for drug–drug interactions prediction
Zhang X, Wang G, Meng X, et al
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[2022]Deepdrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction
Chen Y, Ma T, Yang X, et al
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[2022]R2-ddi: relation-aware feature refinement for drug–drug interaction prediction
Lin J, Wu L, Zhu J, et al
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[2020]Caster: Predicting drug interactions with chemical substructure representation
Huang K, Xiao C, Hoang T, et al
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[2021]Ssi-ddi: substructure-substructure interactions for drug–drug interaction prediction
Nyamabo AK, Yu H, Shi JY
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[2021]Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network
Nyamabo AK, Yu H, Shi JY
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[2022]Drug-drug interaction prediction with learnable size-adaptive molecular substructures
Nyamabo AK, Yu H, Liu Z, et al
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[2022]Stnn-ddi: a substructure-aware tensor neural network to predict drug–drug interactio
Yu H, Zhao S, Shi J
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[2022]Molecular substructure-aware network for drug-drug interaction prediction
Zhu X, Shen Y, Lu W
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[2022]3dgt-ddi: 3d graph and text based neural network for drug–drug interaction prediction
He H, Chen G, Yu-Chian Chen C
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[2023]Dsn-ddi: an accurate and generalized framework for drug–drug interaction prediction by dual-view representation learning
Li Z, Zhu S, Shao B, et al
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[2023]A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
Ma M, Lei X
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[2014]Deepwalk: Online learning of social representations
Perozzi B, Al-Rfou R, Skiena S
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[2015]Grarep: Learning graph representations with global structural information
Cao S, Lu W, Xu Q
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[2015]Line: Large-scale information network embedding
Ribeiro LF, Saverese PH, Figueiredo DR
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[2016]node2vec: Scalable feature learning for networks
Grover A, Leskovec J
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[2016]Asymmetric transitivity preserving graph embedding
Ou M, Cui P, Pei J, et al
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[2016]Variational graph auto-encoders
Kipf TN, Welling M
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[2017]Structural deep network embedding
Wang D, Cui P, Zhu W
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[2017]struc2vec: Learning node representations from structural identity
Ribeiro LF, Saverese PH, Figueiredo DR
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[2018]Feature-derived graph regularized matrix factorization for predicting drug side effects
Zhang W, Liu X, Chen Y, et al
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[2019]Detecting drug communities and predicting comprehensive drug–drug interactions via balance regularized semi-nonnegative matrix factorization
Shi JY, Mao KT, Yu H, et al
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[2018]Modeling polypharmacy side effects with graph convolutional networks
Zitnik M, Agrawal M, Leskovec J
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[2019]LR-GNN: a graph neural network based on link representation for predicting molecular associations
Xu N, Wang P, Chen L, et al
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[2020]Skipgnn: predicting molecular interactions with skip-graph networks
Huang K, Xiao C, Glass LM, et al
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[2021]Predicting biomedical interactions with higher-order graph convolutional networks
Kishan K, Li R, Cui F, et al
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[2022]Directed graph attention networks for predicting asymmetric drug–drug interactions
Feng YY, Yu H, Feng YH, et al
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[2022]Directed graph attention networks for predicting asymmetric drug–drug interactions
Feng YY, Yu H, Feng YH, et al
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[2019]Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-lstm network
Karim MR, Cochez M, Jares JB, et al
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[2020]Kgnn: Knowledge graph neural network for drug-drug interaction prediction
Lin X, Quan Z, Wang ZJ, et al
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[2021]Drug–drug interaction prediction with wasserstein adversarial autoencoder-based knowledge graph embeddings
Dai Y, Guo C, Guo W, et al
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[2021]Sumgnn: multi-typed drug interaction prediction via efficient knowledge graph summarization
Yu Y, Huang K, Zhang C, et al
paper | code
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[2022]Link-aware graph attention network for drug-drug interaction prediction
Hong Y, Luo P, Jin S, et al
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[2022]Attention-based knowledge graph representation learning for predicting drug-drug interactions
Su X, Hu L, You Z, et al
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[2020]Biobert: a pre-trained biomedical language representation model for biomedical text mining
Lee J, Yoon W, Kim S, et al
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[2020]Gognn: Graph of graphs neural network for predicting structured entity interactions
Wang H, Lian D, Zhang Y, et al
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[2021]Muffin: multi-scale feature fusion for drug–drug interaction prediction
Chen Y, Ma T, Yang X, et al
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[2021]Multi-view graph contrastive representation learning for drug-drug interaction prediction
Wang Y, Min Y, Chen X, et al
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[2022]Amde: A novel attention-mechanism-based multidimensional feature encoder for drug–drug interaction prediction
Pang S, Zhang Y, Song T, et al
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[2014]Deepwalk: Online learning of social representations
Perozzi B, Al-Rfou R, Skiena S
-
[2015]Grarep: Learning graph representations with global structural information
Cao S, Lu W, Xu Q
-
[2016]Variational graph auto-encoders
Kipf TN, Welling M
-
[2018]Deep learning improves prediction of drug– drug and drug–food interactions
Ryu JY, Kim HU, Lee SY
-
[2019]Mr-gnn: Multi-resolution and dual graph neural network for predicting structured entity interactions
Xu N, Wang P, Chen L, et al
-
[2020]Caster: Predicting drug interactions with chemical substructure representation
Huang K, Xiao C, Hoang T, et al
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[2020]Kgnn: Knowledge graph neural network for drug-drug interaction prediction
Lin X, Quan Z, Wang ZJ, et al
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[2022]Molecular substructure-aware network for drug-drug interaction prediction
Zhu X, Shen Y, Lu W
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[2022]Raneddi: Relation-aware network embedding for drug-drug interaction prediction
Yu H, Dong WM, Shi JY