  The drug discovery and development process is a long and expensive one,
costing over 1 billion USD on average per drug and taking 10-15 years. To
reduce the high levels of attrition throughout the process, there has been a
growing interest in applying machine learning methodologies to various stages
of drug discovery and development in the recent decade, especially at the
earliest stage identification of druggable disease genes. In this paper, we
have developed a new tensor factorisation model to predict potential drug
targets (genes or proteins) for treating diseases. We created a three
dimensional data tensor consisting of 1,048 gene targets, 860 diseases and
230,011 evidence attributes and clinical outcomes connecting them, using data
extracted from the Open Targets and PharmaProjects databases. We enriched the
data with gene target representations learned from a drug discovery oriented
knowledge graph and applied our proposed method to predict the clinical
outcomes for unseen gene target and disease pairs. We designed three evaluation
strategies to measure the prediction performance and benchmarked several
commonly used machine learning classifiers together with Bayesian matrix and
tensor factorisation methods. The result shows that incorporating knowledge
graph embeddings significantly improves the prediction accuracy and that
training tensor factorisation alongside a dense neural network outperforms all
other baselines. In summary, our framework combines two actively studied
machine learning approaches to disease target identification, namely tensor
factorisation and knowledge graph representation learning, which could be a
promising avenue for further exploration in data driven drug discovery.
