Drug2vec: A Drug Embedding Method with Drug-Drug Interaction as the Context

The combinatorial pharmacological effects of drugs are influenced by the complex interactions among them. When the patient is allergic to some drugs, the combination of drugs have to be changed. Based on that, we put forward a linear-algebra-equation query task. In this paper, we propose a model called Drug2vec that approximates the relationship among drugs and can solve the linear-algebra-equation query task. For example, we can find drugs with the following relationship: drug A + drug B = drug C. Drug2vec applies a three-layer neural network, which firstly projects a drug into an embedded space and then retrieves another drug that interacts with it. Experimental results show that Drug2vec can approximate the relationship among drugs to linear equations, and the drugs that fit a linear equation have connections with respect to their structures. We also propose a metric called AUE (area under the enrichment curve) to evaluate the performance of our model. Drug2vec can predict drug-drug interactions with high accuracy, and the AUE can be 0.96 in the normal test. The AUE score of Drug2vec can be greatly increased with linear modification in the blind test.

[1]  Eric R. LaRose,et al.  Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure , 2017, JMIR medical informatics.

[2]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[3]  Ehsaneddin Asgari,et al.  Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics , 2015, PloS one.

[4]  Gang Fu,et al.  PubChem Substance and Compound databases , 2015, Nucleic Acids Res..

[5]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[6]  Siu-Ming Yiu,et al.  Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization , 2018, BMC Systems Biology.

[7]  Deyu Zhou,et al.  Position-aware deep multi-task learning for drug-drug interaction extraction , 2018, Artif. Intell. Medicine.

[8]  R. Altman,et al.  Data-Driven Prediction of Drug Effects and Interactions , 2012, Science Translational Medicine.

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

[10]  Hao Wang,et al.  IVS2vec: A tool of Inverse Virtual Screening based on word2vec and deep learning techniques. , 2019, Methods.

[11]  Dongsup Kim,et al.  FP2VEC: a new molecular featurizer for learning molecular properties , 2019, Bioinform..

[12]  Jianying Hu,et al.  Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects , 2015, Scientific Reports.

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