Accurate　prediction　of　Drug-Target　Interactions　(DTI)　is　crucial　for　drug　development.　Current　state-of-the-art　deep　learning　methods　have　significantly　advanced　the　field;　however,　these　methods　exhibit　limitations　in　predictive　performance　and　the　propensity　for　false　negatives.　Therefore,　we　propose　EADTN,　a　simple　and　efficient　ensemble　model.　We　have　designed　an　innovative　feature　adaptation　technique　to　automatically　extract　local　weights　of　drugs　and　targets,　and　we　utilize　clustering-enhanced　parameter　fine-tuning　to　overcome　the　issue　of　false　negatives,　thereby　enhancing　its　reliability　in　drug　discovery.　Based　on　EADTN,　we　also　propose　a　Shapley　value-based　method　for　identifying　key　drug　substructures,　effectively　enhancing　the　model＇s　interpretability.　Additionally,　we　utilized　EADTN　to　reveal　potential　interactions　between　NQO1　targets　and　the　drugs　SIRT-IN-1　and　LY2183240,　which　were　subsequently　validated　through　wet-lab　experiments.　Experimental　evidence　demonstrates　that　EADTN　consistently　outperforms　existing　best-performing　models　across　various　data　sets,　promising　significant　benefits　in　fields　such　as　drug　repositioning.