NDLI: DPDDI: a deep predictor for drug-drug interactions

Content Provider	Springer Nature : BioMed Central
Author	Feng, Yue-Hua Zhang, Shao-Wu Shi, Jian-Yu
Abstract	Background The treatment of complex diseases by taking multiple drugs becomes increasingly 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 to obtain and not available in many cases. Results In this work, we presented a novel method (namely DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN), and the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs by capturing the topological relationship 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. Experiment results show that, the newly proposed DPDDI method outperforms four other state-of-the-art methods; the GCN-derived latent features include more DDI information than other features derived from chemical, biological or anatomical properties of drugs; and the concatenation feature aggregation operator is better than two other feature aggregation operators (i.e., inner product and summation). The results in case studies confirm that DPDDI achieves reasonable performance in predicting new DDIs. Conclusion We proposed an effective and robust method DPDDI to predict the potential DDIs by utilizing the DDI network information without considering the drug properties (i.e., drug chemical and biological properties). The method should also be useful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-020-03724-x.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-020-03724-x
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2020-09-24
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Drug-drug interaction DDI prediction Graph convolution network (GCN) Feature extraction Deep neural network Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug–drug interaction prediction

Multi-type feature fusion based on graph neural network for drug-drug interaction prediction

CNN-DDI: a learning-based method for predicting drug–drug interactions using convolution neural networks

DL-PPI: a method on prediction of sequenced protein–protein interaction based on deep learning

A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

Multi-view feature representation and fusion for drug-drug interactions prediction

Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction

A neural network-based method for polypharmacy side effects prediction

MTAGCN: predicting miRNA-target associations in Camellia sinensis var. assamica through graph convolution neural network

DPDDI: a deep predictor for drug-drug interactions

Similar Documents

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug–drug interaction prediction

Multi-type feature fusion based on graph neural network for drug-drug interaction prediction

CNN-DDI: a learning-based method for predicting drug–drug interactions using convolution neural networks

DL-PPI: a method on prediction of sequenced protein–protein interaction based on deep learning

A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

Multi-view feature representation and fusion for drug-drug interactions prediction

Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction

A neural network-based method for polypharmacy side effects prediction

MTAGCN: predicting miRNA-target associations in Camellia sinensis var. assamica through graph convolution neural network

DPDDI: a deep predictor for drug-drug interactions