Drug-target Binding Affinity Prediction by Multi-layer Bidirectional Fusion Molecular Feature
Wenliang Zeng, Zhang Longxin · Computer Science and Technology · 2023
Traditional drug development requires a long process. Accurate prediction of drug-target binding affinity (DTA) by computer can greatly accelerate the drug development process. The key to predicting DTA is how to accurately mine the potential features of drugs and targets. To solve this problem, this paper proposes a DTA prediction model based on a multi-layer structure fused with bidirectional target features (MBDTA). DTA is predicted by this model through exploiting the representation of deep features of the drug-target pairs. MBDTA is split into three steps: Firstly, the initial features are obtained by encoding the drug molecules and targets through label encoding; Secondly, the initial features are fed into the graph neural network module and the recurrent neural network module to learn the potential features in them, respectively; Finally, the two sets of potential features are integrated and then the DTA is predicted exploiting a fully connected layer. Experimental results on the KIBA dataset show that MBDTA improves performance by an average of 32.42%, 3.84%, and 23.64% on the three metrics, MSE, CI, and r2 m , compared to the current state-of-the-art DTA prediction model.