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Recently, deep learning has become the mainstream methodology for drug–target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug–target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers.

We propose a novel Multi-Functional and Robust Drug–Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual …