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.
Research Support, Non-U.S. Gov't
Computational Biology / methods*
Molecular Targeted Therapy*
Pharmaceutical Preparations / metabolism*
K. Buza was supported by the project ED_18-1-2019-0030. Project no. ED_18-1-2019-0030 (Application domain specific highly reliable IT solutions subprogramme) has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme funding scheme. K. Buza received the "Professor Ferencz Rado" Fellowship of the Babes-Bolyai University, Cluj Napoca, Romania. L. Peska was supported by Charles University grant Progres Q48. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.