Abstract
Computational prediction of drug-target interactions is an essential task with various applications in the pharmaceutical industry, such as adverse effect prediction or drug repositioning. Recently, expert systems based on machine learning have been applied to drug-target interaction prediction.
Although hubness-aware machine learning techniques are among the most promising approaches, their potential to enhance drug-target interaction prediction methods has not been exploited yet. In this paper, we extend the Bipartite Local Model (BLM), one of the most prominent interaction prediction methods.
In particular, we use BLM with a hubness-aware regression technique, ECkNN. We represent drugs and targets in the similarity space with rich set of features (i.e., chemical, genomic and interaction features), and build a projection-based ensemble of BLMs.
In order to assist reproducibility of our work as well as comparison to published results, we perform experiments on widely used publicly available drug-target interaction datasets. The results show that our approach outperforms state-of-the-art drug-target prediction techniques.
Additionally, we demonstrate the feasibility of predictions from the point of view of applications.