Drug–target affinity prediction method based on multi-scale information interaction and graph optimization
Drug–target affinity (DTA) prediction as an emerging and effective method is widely applied to explore the strength of drug–target interactions in drug development research. By predicting these interactions, researchers can assess the potential efficacy and safety of candidate drugs at an early stage, narrowing down the search space for therapeutic targets and accelerating the discovery and development of new drugs. However, existing DTA prediction models mainly use graphical representations of drug molecules, which lack information on interactions between individual substructures, thus affecting prediction accuracy and model interpretability.
The dataset used in this paper is KIBA，Davis, Metz and full_toxcast. The way to obtain the above datasets is given in the data file.
This article is implemented by Pytorch.
- PyTorch 1.7.1
- Some other libraries are listed in the requirements file.