Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery

05/17/2021
by   Stephen Bonner, et al.
21

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 potential to assist in key challenges such as target identification. In the drug discovery domain, KGs can be employed as part of a process which can result in lab-based experiments being performed, or impact on other decisions, incurring significant time and financial costs and most importantly, ultimately influencing patient healthcare. For KGE models to have impact in this domain, a better understanding of not only of performance, but also the various factors which determine it, is required. In this study we investigate, over the course of many thousands of experiments, the predictive performance of five KGE models on two public drug discovery-oriented KGs. Our goal is not to focus on the best overall model or configuration, instead we take a deeper look at how performance can be affected by changes in the training setup, choice of hyperparameters, model parameter initialisation seed and different splits of the datasets. Our results highlight that these factors have significant impact on performance and can even affect the ranking of models. Indeed these factors should be reported along with model architectures to ensure complete reproducibility and fair comparisons of future work, and we argue this is critical for the acceptance of use, and impact of KGEs in a biomedical setting. To aid reproducibility of our own work, we release all experimentation code.

READ FULL TEXT

page 8

page 9

page 10

page 12

research
02/19/2021

A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

Drug discovery and development is an extremely complex process, with hig...
research
08/30/2021

Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding

Drug repurposing is more relevant than ever due to drug development's ri...
research
12/13/2021

Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

Improving on the standard of care for diseases is predicated on better t...
research
05/20/2021

Predicting Potential Drug Targets Using Tensor Factorisation and Knowledge Graph Embeddings

The drug discovery and development process is a long and expensive one, ...
research
05/31/2023

Research And Implementation Of Drug Target Interaction Confidence Measurement Method Based On Causal Intervention

The identification and discovery of drug-target Interaction (DTI) is an ...
research
11/20/2021

Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs

For Artificial Intelligence to have a greater impact in biology and medi...
research
11/28/2020

Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19

Previous work established skip-gram word2vec models could be used to min...

Please sign up or login with your details

Forgot password? Click here to reset