Similarity-Adaptive interaction routing for Drug-Target affinity prediction

Drug-target affinity (DTA) prediction is an important task in computer-aided drug discovery. Existing methods usually use a fixed drug-protein interaction strategy. This design is hard to adapt to heterogeneous samples. It is also limited in cold-start and low-similarity scenarios. This study proposes SIGMA-DTA, a similarity-guided adaptive interaction modeling framework for DTA prediction. The framework uses drug and protein similarities as explicit reasoning priors. It introduces a similarity-driven routing mechanism. The mechanism assigns weights to different interaction paths according to the distance between a sample and the training distribution. This enables sample-specific interaction modeling. Unlike conventional unified interaction schemes, SIGMA-DTA adjusts information propagation under different similarity levels. It can model complex drug-target relationships more effectively. Experiments on the Davis and KIBA datasets verify the effectiveness of the method. The results show that integrating similarity information into the interaction modeling process improves robustness and generalization in DTA prediction.