  Drug-target interaction (DTI) prediction is a critical component of the drug
discovery process. In the drug development engineering field, predicting novel
drug-target interactions is extremely crucial.However, although existing
methods have achieved high accuracy levels in predicting known drugs and drug
targets, they fail to utilize global protein information during DTI prediction.
This leads to an inability to effectively predict interaction the interactions
between novel drugs and their targets. As a result, the cross-field information
fusion strategy is employed to acquire local and global protein information.
Thus, we propose the siamese drug-target interaction SiamDTI prediction method,
which utilizes a double channel network structure for cross-field supervised
learning.Experimental results on three benchmark datasets demonstrate that
SiamDTI achieves higher accuracy levels than other state-of-the-art (SOTA)
methods on novel drugs and targets.Additionally, SiamDTI's performance with
known drugs and targets is comparable to that of SOTA approachs. The code is
available at https://anonymous.4open.science/r/DDDTI-434D.
