DFinder: a novel end-to-end graph embedding-based method to identify drug–food interactions

Abstract Motivation Drug–food interactions (DFIs) occur when some constituents of food affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic and/or pharmacokinetic processes. Many computational methods have achieved remarkable results in link prediction tasks between biological entities, which show the potential of computational methods in discovering novel DFIs. However, there are few computational approaches that pay attention to DFI identification. This is mainly due to the lack of DFI data. In addition, food is generally made up of a variety of chemical substances. The complexity of food makes it difficult to generate accurate feature representations for food. Therefore, it is urgent to develop effective computational approaches for learning the food feature representation and predicting DFIs. Results In this article, we first collect DFI data from DrugBank and PubMed, respectively, to construct two datasets, named DrugBank-DFI and PubMed-DFI. Based on these two datasets, two DFI networks are constructed. Then, we propose a novel end-to-end graph embedding-based method named DFinder to identify DFIs. DFinder combines node attribute features and topological structure features to learn the representations of drugs and food constituents. In topology space, we adopt a simplified graph convolution network-based method to learn the topological structure features. In feature space, we use a deep neural network to extract attribute features from the original node attributes. The evaluation results indicate that DFinder performs better than other baseline methods. Availability and implementation The source code is available at https://github.com/23AIBox/23AIBox-DFinder. Supplementary information Supplementary data are available at Bioinformatics online.

[1]  R. Jiang,et al.  DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction , 2022, bioRxiv.

[2]  C. Kwoh,et al.  Graph representation learning in bioinformatics: trends, methods and applications , 2021, Briefings Bioinform..

[3]  Xuequn Shang,et al.  Predicting Hepatoma-Related Genes Based on Representation Learning of PPI network and Gene Ontology Annotations , 2021, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[4]  Jian-Yu Shi,et al.  SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction , 2021, Briefings Bioinform..

[5]  Jianye Hao,et al.  An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction , 2021, Briefings Bioinform..

[6]  Jiajie Peng,et al.  Identifying drug-target interactions based on graph convolutional network and deep neural network , 2020, Briefings Bioinform..

[7]  Osmar R. Zaïane,et al.  Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction , 2020, Comput. Biol. Medicine.

[8]  Xin Chen,et al.  GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task. , 2020, Methods.

[9]  Yi Li,et al.  Graph Structural-topic Neural Network , 2020, KDD.

[10]  Bo Liu,et al.  Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome , 2020, Frontiers in Bioengineering and Biotechnology.

[11]  Maoguo Gong,et al.  Graph convolutional networks with multi-level coarsening for graph classification , 2020, Knowl. Based Syst..

[12]  Jiayu Zhou,et al.  Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets (Student Abstract) , 2020, AAAI.

[13]  Shaowu Zhang,et al.  DPDDI: a deep predictor for drug-drug interactions , 2020, BMC Bioinformatics.

[14]  Xiangnan He,et al.  LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation , 2020, SIGIR.

[15]  Jimeng Sun,et al.  CASTER: Predicting Drug Interactions with Chemical Substructure Representation , 2019, AAAI.

[16]  Srinivasan Parthasarathy,et al.  Graph embedding on biomedical networks: methods, applications and evaluations , 2019, Bioinform..

[17]  Jiayu Zhou,et al.  Graph convolutional networks for computational drug development and discovery , 2019, Briefings Bioinform..

[18]  Georgeta Bordea,et al.  FIDEO: Food Interactions with Drugs Evidence Ontology , 2020, ICBO/ODLS.

[19]  Christine M. Madla,et al.  The mechanisms of pharmacokinetic food-drug interactions - A perspective from the UNGAP group. , 2019, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[20]  Pingzhao Hu,et al.  Predicting drug-target interaction network using deep learning model , 2019, Comput. Biol. Chem..

[21]  Nuo Xu,et al.  MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions , 2019, IJCAI.

[22]  Kristina Lerman,et al.  MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing , 2019, ICML.

[23]  Yuan He,et al.  Graph Neural Networks for Social Recommendation , 2019, WWW.

[24]  Damian Szklarczyk,et al.  STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets , 2018, Nucleic Acids Res..

[25]  Joonseok Lee,et al.  N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification , 2018, UAI.

[26]  Zhao Li,et al.  MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction , 2019, IEEE Access.

[27]  Feng Huang,et al.  SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions , 2018, PLoS Comput. Biol..

[28]  Lars Juhl Jensen,et al.  CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision , 2018, bioRxiv.

[29]  Istvan G. Telessy,et al.  Let’s Keep an Eye on Food-Drug Interaction , 2018, Food & Nutrition Journal.

[30]  Jae Yong Ryu,et al.  Deep learning improves prediction of drug–drug and drug–food interactions , 2018, Proceedings of the National Academy of Sciences.

[31]  Jianye Hao,et al.  A learning-based framework for miRNA-disease association identification using neural networks , 2018, bioRxiv.

[32]  Meng Chen,et al.  Food-drug interactions precipitated by fruit juices other than grapefruit juice: An update review , 2018, Journal of food and drug analysis.

[33]  Jure Leskovec,et al.  Modeling polypharmacy side effects with graph convolutional networks , 2018, bioRxiv.

[34]  Sabrina Jaeger,et al.  Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition , 2018, J. Chem. Inf. Model..

[35]  Minyi Guo,et al.  GraphGAN: Graph Representation Learning with Generative Adversarial Nets , 2017, AAAI.

[36]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[37]  Fleur Mougin,et al.  POMELO: Medline corpus with manually annotated food-drug interactions , 2017, BiomedicalNLP@RANLP.

[38]  Gianni Panagiotou,et al.  NutriChem 2.0: exploring the effect of plant-based foods on human health and drug efficacy , 2017, Database J. Biol. Databases Curation.

[39]  Daniel R. Figueiredo,et al.  struc2vec: Learning Node Representations from Structural Identity , 2017, KDD.

[40]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[41]  Max Welling,et al.  Variational Graph Auto-Encoders , 2016, ArXiv.

[42]  Jian Pei,et al.  Asymmetric Transitivity Preserving Graph Embedding , 2016, KDD.

[43]  Wenwu Zhu,et al.  Structural Deep Network Embedding , 2016, KDD.

[44]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[45]  Qiongkai Xu,et al.  GraRep: Learning Graph Representations with Global Structural Information , 2015, CIKM.

[46]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[47]  W. Alkema,et al.  Application of text mining in the biomedical domain. , 2015, Methods.

[48]  Janos X. Binder,et al.  DISEASES: Text mining and data integration of disease–gene associations , 2014, bioRxiv.

[49]  Gianni Panagiotou,et al.  Developing a Molecular Roadmap of Drug-Food Interactions , 2015, PLoS Comput. Biol..

[50]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[51]  A. Senturk,et al.  Food and drug interactions: a general review. , 2014, Acta scientiarum polonorum. Technologia alimentaria.

[52]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[53]  Paloma Martínez,et al.  The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions , 2013, J. Biomed. Informatics.

[54]  Alexander J. Smola,et al.  Distributed large-scale natural graph factorization , 2013, WWW.

[55]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[56]  J. Klawitter,et al.  Low-salt diet and cyclosporine nephrotoxicity: changes in kidney cell metabolism. , 2012, Journal of proteome research.

[57]  P. Neuvonen.  Interactions with the Absorption of Tetracyclines , 2012, Drugs.

[58]  H. Halkin,et al.  Warfarin and vitamin K intake in the era of pharmacogenetics. , 2010, British journal of clinical pharmacology.

[59]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[60]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[61]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[62]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[63]  Hiroshi Mamitsuka,et al.  A probabilistic model for mining implicit 'chemical compound-gene' relations from literature , 2005, ECCB/JBI.

[64]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[65]  M Vuelta,et al.  [Food-drug interactions]. , 1997, Enfermeria intensiva.

[66]  E. Lentjes,et al.  The effect of grapefruit juice on cyclosporine and prednisone metabolism in transplant patients , 1995, Clinical pharmacology and therapeutics.