Deep-Learning-Based Drug–Target Interaction Prediction

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic…

• NN
  National Natural Science Foundation of China

  Awards: 21305163, 21375151, 81402853, 21175157
• MO
  Ministry of Education of the People's Republic of China

  Award: 2015zzts163