Drug-target interaction (DTI) and affinity (DTA) predictors increasingly achieve strong benchmark scores, yet their internal use of sequence, fingerprint, and graph features often remains opaque. We present an interpretability audit of BridgeDPI architecture on three different datasets including Gao, Human, and C.elegans. This study combines gradient-based attributions -- integrated gradients, saliency, layer-wise relevance propagation, SmoothGrad, and SmoothGrad-IG -- with feature-wise occlusion ablation and strict intersection consensus across methods to reduce single-explainer bias. We summarize sensitivity and signed effects at raw inputs, at the bridge similarity scaffold, and through the graph convolution, including edge-level sensitivities and targeted edge removals. The results show that explainability is most informative when treated as model criticism: it reveals modality dominance, padding and special-token artifacts, dataset-dependent cooperative versus suppressive effects across layers, and chemistry-consistent fragment and composition motifs where methods agree. These analyses do not substitute for structural or experimental ground truth, yet they can provide testable hypotheses for downstream validation in computational drug discovery pipelines. More broadly, applying modern XAI to contemporary DTI/DTA models is still an early pass over the rich structure implicit in trained weights and data -- yet even this first layer of scrutiny already helps researchers relate predictions to drug- and target-side representations and to prioritize external validation.

翻译：药物-靶标相互作用（DTI）及亲和力（DTA）预测器在基准测试中取得越来越高的分数，但其对序列、指纹和图特征的内部使用方式往往仍不透明。我们对BridgeDPI架构在包括Gao、Human和C.elegans三个不同数据集上的可解释性进行审计。这项研究结合了基于梯度的归因方法——积分梯度、显著性、逐层相关性传播、SmoothGrad和SmoothGrad-IG——与特征级遮挡消融及跨方法的严格交集共识，以减少单一解释器的偏差。我们总结了原始输入、桥相似性支架以及图卷积过程中的敏感性和符号效应，包括边级敏感性和定向边移除。结果表明，当将可解释性视为模型批判时，其信息量最大：它揭示了模态主导性、填充和特殊标记伪影、跨数据集的层间合作与抑制效应，以及方法一致时化学上一致的片段与组合基序。这些分析无法替代结构或实验真实性，但可为计算药物发现流程中的下游验证提供可测试的假设。更广泛而言，将现代XAI应用于当代DTI/DTA模型仍是对训练权重和数据中隐含丰富结构的初步探索——但即便是这第一层审视，已可帮助研究者将预测结果关联到药物和靶标侧表征，并优先安排外部验证。