Mamba-DTA: Drug-Target Binding Affinity Prediction with State Space Model

Biochemical methods for measuring drug-target binding are costly and slow, while deep learning offers a crucial solution. Deep learning methods for predicting drug-target binding affinity have achieved remarkable success, but existing approaches face challenges: adjacent elements in a three-dimensional structure may be far apart in a one-dimensional representation, and long sequences of drug and target data often contain lots of noise. In this paper, we introduce MambaDTA, a novel architecture for drug-target affinity prediction based on the State Space Model (SSM). Mamba-DTA utilizes SSM to model the drug molecules and target molecules and extract more discriminative spatial structural features efficiently and stably. Additionally, we design Interaction-based Selective Filtering (ISF) module to model drug-target interactions and filter out redundant information. The experimental results on two publicly available datasets, namely Davis and KIBA, demonstrate the effectiveness and superiority of our Mamba-DTA. Specifically, Mamba-DTA achieves a relative gain of 13.3% in terms of MAE on the Davis dataset. Source codes are available at https://github.com/202324131016T/Mamba-DTA.