Predicting Drug Drug Interactions by Signed Graph Filtering-Based Convolutional Networks

AbstractMotivation: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.Results: Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.Availability: Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.Contact:[email protected]

Download Full-text

GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33011126 ◽

2019 ◽

Vol 33 ◽

pp. 1126-1133 ◽

Cited By ~ 9

Author(s):

Junyuan Shang ◽

Cao Xiao ◽

Tengfei Ma ◽

Hongyan Li ◽

Jimeng Sun

Keyword(s):

Drug Interactions ◽

State Of The Art ◽

Adverse Outcomes ◽

Health Conditions ◽

Personalized Recommendation ◽

Rate Reduction ◽

Memory Module ◽

Convolutional Networks ◽

Health History ◽

Patient Health

Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.

Download Full-text

Identifying Side Effects of Some Drugs Used in Covid-19 Treatment

10.21203/rs.3.rs-40903/v1 ◽

2020 ◽

Author(s):

Irfan Aygün ◽

Mehmet Kaya ◽

Reda Alhajj

Keyword(s):

Side Effects ◽

Drug Interactions ◽

Clinical Studies ◽

Experimental Results ◽

Negative Effects ◽

Convolutional Networks ◽

Organ Systems ◽

Selection Of

Abstract To increase the success in Covid 19 treatment, many drug suggestions are presented, and some clinical studies are shared in the literature. There have been some attempts to use some of these drugs in combination. However, using more than one drug together may cause serious side effects on patients. Therefore, detecting drug-drug interactions of the drugs used will be of great importance in the treatment of Covid 19. In this study, the interactions of 8 drugs used for Covid 19 treatment with 645 different drugs and possible side effects estimates have been produced using Graph Convolutional Networks. Organ systems and diseases in which these 8 drugs cause the most negative effects have been identified. In addition, as it is known that some of these 8 drugs are used together in Covid-19 treatment, the side effects caused by using these drugs together are shared. With the experimental results obtained, it is aimed to facilitate the selection of the drugs and increase the success of Covid 19 treatment according to the targeted patient.

Download Full-text

Signed Graph Convolutional Networks

2018 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm.2018.00113 ◽

2018 ◽

Cited By ~ 32

Author(s):

Tyler Derr ◽

Yao Ma ◽

Jiliang Tang

Keyword(s):

Signed Graph ◽

Convolutional Networks

Download Full-text

MTDDI: a graph convolutional network framework for predicting Multi-Type Drug-Drug Interactions

10.21203/rs.3.rs-397281/v1 ◽

2021 ◽

Author(s):

YueHua Feng ◽

Shao-Wu Zhang ◽

Qing-Qing Zhang ◽

Chu-Han Zhang ◽

Jian-Yu Shi

Keyword(s):

Deep Learning ◽

Drug Interactions ◽

Adverse Reactions ◽

Underlying Mechanism ◽

Structural Relationship ◽

Convolutional Network ◽

Convolutional Networks ◽

Machine Learning Methods ◽

Single Fold ◽

Predictive Methods

Abstract Although the polypharmacy has both higher therapeutic efficacy and less drug resistance in combating complex diseases, drug-drug interactions (DDIs) may trigger unexpected pharmacological effects, such as side effects, adverse reactions, or even serious toxicity. Thus, it is crucial to identify DDIs and explore its underlying mechanism (e.g., DDIs types) for polypharmacy safety. However, the detection of DDIs in assays is still time-consuming and costly, due to the need of experimental search over a large drug combinational space. Machine learning methods have been proved as a promising and efficient method for preliminary DDI screening. Most shallow learning-based predictive methods focus on whether a drug interacts with another or not. Although deep learning (DL)-based predictive methods address a more realistic screening task for identifying the DDI types, they only predict the DDI types of known DDI, ignoring the structural relationship between DDI entries, and they also cannot reveal the knowledge about the dependence between DDI types. Thus, here we proposed a novel end-to-end deep learning-based predictive method (called MTDDI) to predict DDIs as well as its types, exploring the underlying mechanism of DDIs. MTDDI designs an encoder derived from enhanced deep relational graph convolutional networks to capture the structural relationship between multi-type DDI entries, and adopts the tensor-like decoder to uniformly model both single-fold interactions and multi-fold interactions to reflect the relation between DDI types. The results show that our MTDDI is superior to other state-of-the-art deep learning-based methods. For predicting the multi-type DDIs with unknown DDIs in case of both single-fold DDIs and multi-fold DDIs, we validated the effectiveness and the practical capability of our MTDDI. More importantly, MTDDI can reveal the dependency between DDI types. These crucial observations are beneficial to uncover the mechanism and regularity of DDIs.

Download Full-text

Identifying side effects of commonly used drugs in the treatment of Covid 19

Scientific Reports ◽

10.1038/s41598-020-78697-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

İrfan Aygün ◽

Mehmet Kaya ◽

Reda Alhajj

Keyword(s):

Side Effects ◽

Drug Interactions ◽

Cardiovascular System ◽

Clinical Studies ◽

Adverse Reactions ◽

Experimental Results ◽

Hematopoietic System ◽

Convolutional Networks ◽

Selection Of

AbstractTo increase the success in Covid 19 treatment, many drug suggestions are presented, and some clinical studies are shared in the literature. There have been some attempts to use some of these drugs in combination. However, using more than one drug together may cause serious side effects on patients. Therefore, detecting drug-drug interactions of the drugs used will be of great importance in the treatment of Covid 19. In this study, the interactions of 8 drugs used for Covid 19 treatment with 645 different drugs and possible side effects estimates have been produced using Graph Convolutional Networks. As a result of the experiments, it has been found that the hematopoietic system and the cardiovascular system are exposed to more side effects than other organs. Among the focused drugs, Heparin and Atazanavir appear to cause more adverse reactions than other drugs. In addition, as it is known that some of these 8 drugs are used together in Covid-19 treatment, the side effects caused by using these drugs together are shared. With the experimental results obtained, it is aimed to facilitate the selection of the drugs and increase the success of Covid 19 treatment according to the targeted patient.

Download Full-text