Discovering protein drug targets using knowledge graph embeddings

SK Mohamed, V Nováček, A Nounu - Bioinformatics, 2020 - academic.oup.com
Motivation Computational approaches for predicting drug–target interactions (DTIs) can
provide valuable insights into the drug mechanism of action. DTI predictions can help to …

Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

R Celebi, H Uyar, E Yasar, O Gumus, O Dikenelli… - BMC …, 2019 - Springer
Background Current approaches to identifying drug-drug interactions (DDIs), include safety
studies during drug development and post-marketing surveillance after approval, offer …

[PDF][PDF] KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction.

X Lin, Z Quan, ZJ Wang, T Ma, X Zeng - IJCAI, 2020 - xuanlin1991.github.io
Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and
clinical application, and effectively identifying potential D-DIs during clinical trials is critical …

Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network

MR Karim, M Cochez, JB Jares, M Uddin… - Proceedings of the 10th …, 2019 - dl.acm.org
Interference between pharmacological substances can cause serious medical injuries.
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …

Attention-based knowledge graph representation learning for predicting drug-drug interactions

X Su, L Hu, Z You, P Hu, B Zhao - Briefings in bioinformatics, 2022 - academic.oup.com
Drug–drug interactions (DDIs) are known as the main cause of life-threatening adverse
events, and their identification is a key task in drug development. Existing computational …

Toward better drug discovery with knowledge graph

X Zeng, X Tu, Y Liu, X Fu, Y Su - Current opinion in structural biology, 2022 - Elsevier
Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …

[HTML][HTML] Understanding the performance of knowledge graph embeddings in drug discovery

S Bonner, IP Barrett, C Ye, R Swiers, O Engkvist… - Artificial Intelligence in …, 2022 - Elsevier
Abstract Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE)
models have recently begun to be explored in the context of drug discovery and have the …

A unified drug–target interaction prediction framework based on knowledge graph and recommendation system

Q Ye, CY Hsieh, Z Yang, Y Kang, J Chen, D Cao… - Nature …, 2021 - nature.com
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various
areas, such as virtual screening, drug repurposing and identification of potential drug side …

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective

S Bonner, IP Barrett, C Ye, R Swiers… - Briefings in …, 2022 - academic.oup.com
Drug discovery and development is a complex and costly process. Machine learning
approaches are being investigated to help improve the effectiveness and speed of multiple …

KG-Predict: A knowledge graph computational framework for drug repurposing

Z Gao, P Ding, R Xu - Journal of biomedical informatics, 2022 - Elsevier
The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data
has offered unprecedented opportunities for drug discovery including drug repurposing …