State-of-the-art approaches for the prediction of drug-target interactions (DTI) are
based on various techniques, such as matrix factorisation, restricted Boltzmann machines,
network-based inference and bipartite local models (BLM). In this paper, we propose
the framework of Asymmetric Loss Models (ALM) which is more consistent with the underlying
chemical reality compared with conventional regression techniques. Furthermore, we
propose to use an asymmetric loss model with BLM to predict drug-target interactions
accurately. We evaluate our approach on publicly available real-world drug-target
interaction datasets. The results show that our approach outperforms state-of-the-art
DTI techniques, including recent versions of BLM.
