Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

Takeda, Takako; Ming, Hao; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli

doi:10.1186/s13321-017-0200-8

Cited by 80 publications

(58 citation statements)

References 29 publications

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“…Similarly, DDIs using phenotypic, chemical, biological, therapeutic, structural, and genomic similarities of drugs are used for predicting DDIs [9]. Other investigations used pharmacological and graph qualities between drugs [7] or drug structural similarities and interaction networks incorporating PK and PD knowledge [41] using LR. Peng et al [28] developed a Bayesian network, which combines molecular drug similarity and drug side-effect similarity to predict the combined effect of drugs.…”

Section: Related Workmentioning

confidence: 99%

Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network

Karim

Cochez

Jares

et al. 2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Interference between pharmacological substances can cause serious medical injuries. Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases but can also result in a reduction of drug development cost. Presently, most drug-related knowledge is the result of clinical evaluations and post marketing surveillance; resulting in a limited amount of information. Existing data-driven prediction approaches for DDIs typically rely on a single source of information, while using information from multiple sources would help improve predictions. Machine learning (ML) techniques are used, but the techniques are often unable to deal with skew in the data. Hence, we propose a new ML approach for predicting DDIs based on multiple data sources. For this task we use 12,000 drug features from DrugBank, PharmGKB, and KEGG drugs, which are integrated using Knowledge Graphs (KGs). To train our prediction model, we first embed the nodes in the graph using various embedding approaches. We found that the best performing combination was a ComplEx embedding method creating using PyTorch-BigGraph (PBG) with a Convolutional-LSTM network and classic machine learning based prediction models. The model averaging ensemble method of three best classifiers yields up to 0.94, 0.92, 0.80 for AUPR, F1-score, and MCC, respectively during 5-fold cross-validation tests.

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Section: Related Workmentioning

confidence: 99%

Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network

Karim

Cochez

Jares

et al. 2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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“…However, unexpected DDI can also trigger side effects, adverse reactions, and even serious toxicity. They lead to patients in danger [2]. As the need of multi-drug treatments is increasing, the identification of DDIs is urgent.…”

Section: Introductionmentioning

confidence: 99%

DPDDI:a Deep Predictor for Drug-Drug Interactions

Feng

Zhang

Shi

2020

Preprint

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Background: The treatment of complex diseases taking multiple drugs becomes popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly obtained and not available in many cases. Results: In this work, we present a novel method (called DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN) and constructing the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs for capturing the topological relationship to their neighborhood of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. The experiment results show that our DPDDI outperforms other four state-of-the-art methods; the GCN-derived latent features greatly outperform other features derived from chemical, biological or anatomical properties of drugs; the concatenation feature aggregation operator is better than other two feature aggregation operators (i.e., inner product and summation). The results in case studies indicates that DPDDI has the good capability for predicting the new DDIs. Conclusion—We propose an effective and robust method of DPDDI to predict the potential DDIs, which just utilizes the DDI network information, working well without drug properties (i.e., drug chemical and biological properties). It can be expected that DPDDI can be helpful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.

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“…Some approaches harness the idea that similar drugs will take part in similar drug interactions 6 . For this purpose, modeling of chemical and structural similarities has been explored 7,8 . Other approaches employ unstructured text, such as the Medline biomedical literature corpus, to extract interaction information with text mining techniques 9 .…”

Section: Introductionmentioning

confidence: 99%

Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications

Burkhardt

Subramanian

Mower

et al. 2019

Preprint

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The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount of research has been conducted in this space. Recent work has successfully applied a method of deriving distributed vector representations from structured biomedical knowledge, known as Embedding of Semantic Predications (ESP), to the problem of predicting individual drug side effects. In the current paper we extend this work by applying ESP to the problem of predicting polypharmacy side-effects for particular drug combinations, building on a recent reconceptualization of this problem as a network of drug nodes connected by side effect edges. We evaluate ESP embeddings derived from the resulting graph on a side-effect prediction task against a previously reported graph convolutional neural network approach, using the same data and evaluation methods. We demonstrate that ESP models perform better, while being faster to train, more re-usable, and significantly simpler.

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Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

Cited by 80 publications

References 29 publications

Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network

Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network

DPDDI:a Deep Predictor for Drug-Drug Interactions

Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications

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