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 framework named DeepDTIs. It first abstracts representations
from raw input descriptors using unsupervised pretraining and then
applies known label pairs of interaction to build a classification
model. Compared with other methods, it is found that DeepDTIs reaches
or outperforms other state-of-the-art methods. The DeepDTIs can be
further used to predict whether a new drug targets to some existing
targets or whether a new target interacts with some existing drugs.