Deep Drug–Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion

Accurate drug–target binding affinity (DTA) prediction
is
crucial in drug discovery. Recently, deep learning methods for DTA
prediction have made significant progress. However, there are still
two challenges: (1) recent models always ignore the correlations in
drug and target data in the drug/target representation process and
(2) the interaction learning of drug–target pairs always is
by simple concatenation, which is insufficient to explore their fusion.
To overcome these challenges, we propose an end-to-end sequence-based
model called BTDHDTA. In the feature extraction process, the bidirectional
gated recurrent unit (GRU), transformer encoder, and dilated convolution
are employed to extract global, local, and their correlation patterns
of drug and target input. Additionally, a module combining convolutional
neural networks with a Highway connection is introduced to fuse drug
and protein deep features. We evaluate the performance of BTDHDTA
on three benchmark data sets (Davis, KIBA, and Metz), demonstrating
its superiority over several current state-of-the-art methods in key
metrics such as Mean Squared Error (MSE), Concordance Index (CI),
and Regression toward the mean (Rm2). The results
indicate that our method achieves a better performance in DTA prediction.
In the case study, we use the BTDHDTA model to predict the binding
affinities between 3137 FDA-approved drugs and severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins,
validating the model’s effectiveness in practical scenarios.