/resources-for-DDIs-prediction-using-DL

related to graph and deep Learning for drug-drug interactions prediction.

A comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

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.

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Datasets

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/

Methods

Chemical structure based

Similarity based

  • [2017]Computational prediction of drug-drug interactions based on drugs functional similarities

    Ferdousi R, Safdari R, Omidi Y

    paper | code

  • [2018]Deep learning improves prediction of drug– drug and drug–food interactions

    Ryu JY, Kim HU, Lee SY

    paper | code

  • [2018]Detection of drug–drug interactions through data mining studies using clinical sources, scientific literature and social media

    Vilar S, Friedman C, Hripcsak G

    paper | code

  • [2019]Deep learning for high-order drug-drug interaction prediction

    Chen Y, Ma T, Yang X, et al

    paper | code

  • [2019]Mlrda: A multi-task semi-supervised learning framework for drug-drug interaction prediction

    Chu X, Lin Y, Wang Y, et al

    paper | code

  • [2019]Kmr: knowledge-oriented medicine representation learning for drug–drug interaction and similarity computation

    Shen Y, Yuan K, Yang M, et al

    paper | code

  • [2020]A multimodal deep learning framework for predicting drug–drug interaction events

    Deng Y, Xu X, Qiu Y, et al

    paper | code

  • [2021]Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties

    Feixiong Cheng, Zhongming Zhao

    paper | code

  • [2021]Moltrans: Molecular interaction transformer for drug–target interaction prediction

    Huang K, Xiao C, Glass LM, et al

    paper | code

  • [2022]Deside-ddi: interpretable prediction of drug-drug interactions using drug-induced gene expressions

    Deng Y, Xu X, Qiu Y, et al

    paper | code

  • [2022]Mddi-scl: predicting multi-type drug-drug interactions via supervised contrastive learning

    Lin S, Chen W, Chen G, et al

    paper | code

Molecular graph

  • [2019]Mr-gnn: Multi-resolution and dual graph neural network for predicting structured entity interactions

    Xu N, Wang P, Chen L, et al

    paper | code

  • [2019]Drug-drug adverse effect prediction with graph co-attention

    Deac A, Huang YH, Veliˇckovi´c P, et al

    paper | code

  • [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

    paper | code

  • [2022]Multi-type feature fusion based on graph neural network for drug-drug interaction prediction

    He C, Liu Y, Li H, et al

    paper | code

  • [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

    paper | code

  • [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

    paper | code

  • [2022]R2-ddi: relation-aware feature refinement for drug–drug interaction prediction

    Lin J, Wu L, Zhu J, et al

    paper | code

Substructure based

  • [2020]Caster: Predicting drug interactions with chemical substructure representation

    Huang K, Xiao C, Hoang T, et al

    paper | code

  • [2021]Ssi-ddi: substructure-substructure interactions for drug–drug interaction prediction

    Nyamabo AK, Yu H, Shi JY

    paper | code

  • [2021]Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network

    Nyamabo AK, Yu H, Shi JY

    paper | code

  • [2022]Drug-drug interaction prediction with learnable size-adaptive molecular substructures

    Nyamabo AK, Yu H, Liu Z, et al

    paper | code

  • [2022]Stnn-ddi: a substructure-aware tensor neural network to predict drug–drug interactio

    Yu H, Zhao S, Shi J

    paper | code

  • [2022]Molecular substructure-aware network for drug-drug interaction prediction

    Zhu X, Shen Y, Lu W

    paper | code

  • [2022]3dgt-ddi: 3d graph and text based neural network for drug–drug interaction prediction

    He H, Chen G, Yu-Chian Chen C

    paper | code

  • [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

    paper | code

  • [2023]A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions

    Ma M, Lei X

    paper | code

Network based

Graph embedding

  • [2014]Deepwalk: Online learning of social representations

    Perozzi B, Al-Rfou R, Skiena S

    paper | code

  • [2015]Grarep: Learning graph representations with global structural information

    Cao S, Lu W, Xu Q

    paper | code

  • [2015]Line: Large-scale information network embedding

    Ribeiro LF, Saverese PH, Figueiredo DR

    paper | code

  • [2016]node2vec: Scalable feature learning for networks

    Grover A, Leskovec J

    paper | code

  • [2016]Asymmetric transitivity preserving graph embedding

    Ou M, Cui P, Pei J, et al

    paper | code

  • [2016]Variational graph auto-encoders

    Kipf TN, Welling M

    paper | code

  • [2017]Structural deep network embedding

    Wang D, Cui P, Zhu W

    paper | code

  • [2017]struc2vec: Learning node representations from structural identity

    Ribeiro LF, Saverese PH, Figueiredo DR

    paper | code

  • [2018]Feature-derived graph regularized matrix factorization for predicting drug side effects

    Zhang W, Liu X, Chen Y, et al

    paper | code

  • [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

    paper | code

Link prediction

  • [2018]Modeling polypharmacy side effects with graph convolutional networks

    Zitnik M, Agrawal M, Leskovec J

    paper | code

  • [2019]LR-GNN: a graph neural network based on link representation for predicting molecular associations

    Xu N, Wang P, Chen L, et al

    paper | code

  • [2020]Skipgnn: predicting molecular interactions with skip-graph networks

    Huang K, Xiao C, Glass LM, et al

    paper | code

  • [2021]Predicting biomedical interactions with higher-order graph convolutional networks

    Kishan K, Li R, Cui F, et al

    paper | code

  • [2022]Directed graph attention networks for predicting asymmetric drug–drug interactions

    Feng YY, Yu H, Feng YH, et al

    paper | code

  • [2022]Directed graph attention networks for predicting asymmetric drug–drug interactions

    Feng YY, Yu H, Feng YH, et al

    paper | code

Knowledge graph

  • [2019]Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-lstm network

    Karim MR, Cochez M, Jares JB, et al

    paper | code

  • [2020]Kgnn: Knowledge graph neural network for drug-drug interaction prediction

    Lin X, Quan Z, Wang ZJ, et al

    paper | code

  • [2021]Drug–drug interaction prediction with wasserstein adversarial autoencoder-based knowledge graph embeddings

    Dai Y, Guo C, Guo W, et al

    paper | code

  • [2021]Sumgnn: multi-typed drug interaction prediction via efficient knowledge graph summarization

    Yu Y, Huang K, Zhang C, et al

    paper | code

  • [2022]Link-aware graph attention network for drug-drug interaction prediction

    Hong Y, Luo P, Jin S, et al

    paper | code

  • [2022]Attention-based knowledge graph representation learning for predicting drug-drug interactions

    Su X, Hu L, You Z, et al

    paper | code

NLP based

  • [2020]Biobert: a pre-trained biomedical language representation model for biomedical text mining

    Lee J, Yoon W, Kim S, et al

    paper | code

Hybrid method

  • [2020]Gognn: Graph of graphs neural network for predicting structured entity interactions

    Wang H, Lian D, Zhang Y, et al

    paper | code

  • [2021]Muffin: multi-scale feature fusion for drug–drug interaction prediction

    Chen Y, Ma T, Yang X, et al

    paper | code

  • [2021]Multi-view graph contrastive representation learning for drug-drug interaction prediction

    Wang Y, Min Y, Chen X, et al

    paper | code

  • [2022]Amde: A novel attention-mechanism-based multidimensional feature encoder for drug–drug interaction prediction

    Pang S, Zhang Y, Song T, et al

    paper | code

Baseline models

  • [2014]Deepwalk: Online learning of social representations

    Perozzi B, Al-Rfou R, Skiena S

    paper | code

  • [2015]Grarep: Learning graph representations with global structural information

    Cao S, Lu W, Xu Q

    paper | code

  • [2016]Variational graph auto-encoders

    Kipf TN, Welling M

    paper | code

  • [2018]Deep learning improves prediction of drug– drug and drug–food interactions

    Ryu JY, Kim HU, Lee SY

    paper | code

  • [2019]Mr-gnn: Multi-resolution and dual graph neural network for predicting structured entity interactions

    Xu N, Wang P, Chen L, et al

    paper | code

  • [2020]Caster: Predicting drug interactions with chemical substructure representation

    Huang K, Xiao C, Hoang T, et al

    paper | code

  • [2020]Kgnn: Knowledge graph neural network for drug-drug interaction prediction

    Lin X, Quan Z, Wang ZJ, et al

    paper | code

  • [2022]Molecular substructure-aware network for drug-drug interaction prediction

    Zhu X, Shen Y, Lu W

    paper | code

  • [2022]Raneddi: Relation-aware network embedding for drug-drug interaction prediction

    Yu H, Dong WM, Shi JY

    paper | code

    Platform and toolkit

  • DeepChem

  • DeepPurpose

  • PaddleHelix

  • DGL-LifeSci

  • TorchDrug

  • ADMETlab

  • ChemicalX