ALADIN: A New Approach for Drug-Target Interaction Prediction

Abstract

Due to its pharmaceutical applications, one of the most prominent machine learning challenges in bioinformatics is the prediction of drug–target interactions. State-of-the-art approaches are based on various techniques, such as matrix factorization, restricted Boltzmann machines, network-based inference and bipartite local models (BLM). In this paper, we extend BLM by the incorporation of a hubness-aware regression technique coupled with an enhanced representation of drugs and targets in a multi-modal similarity space. Additionally, we propose to build a projection-based ensemble. Our technique (ALADIN) is evaluated on publicly available real-world drug–target interaction datasets. The results show that our approach statistically significantly outperforms BLM-NII, a recent version of BLM, as well as NetLapRLS and WNN-GIP. Code related to this chapter is available at: https://github.com/lpeska/ALADIN Data related to this chapter are available at: https://zenodo.org/record/556337#.WPiAzIVOIdV Supplementary material is available at: http://www.biointelligence.hu/dti/