Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is the first DTI model to simultaneously leverage drug molecule fragments and protein pockets. Our information-rich representations for both proteins and drugs offer a detailed perspective on their interaction. Inspired by the Perceiver IO framework, our model features a learnable latent array, initially interacting with protein binding site embeddings using cross-attention and later refined through self-attention and used as a query to the drug fragments in the drug's cross-attention transformer block. This learnable query array serves as a mediator and enables seamless information translation, preserving critical nuances in drug-protein interactions. Our computational results on three benchmarking datasets demonstrate the superior predictive power of our model over several state-of-the-art models. We also show the interpretability of our model in terms of the critical components of both target proteins and drug molecules within drug-target pairs.

利用FragXsiteDTI模型，同时运用药物分子碎片和蛋白质口袋，优化了药物靶标相互作用预测的模型解释性和性能，其详细的蛋白质和药物的信息表征提供了药物靶标相互作用的详细视角。通过Perceiver IO框架，模型的可学习潜在数组用于蛋白质结合位点嵌入的交叉注意力，并通过自注意力进行调整，作为查询药物分子在药物的交叉注意力变换块中的属性，从而保留了药物-蛋白质相互作用中的关键细微差异，并且在三个基准数据集上的计算结果表明，我们的模型在预测能力上超过了几种先进模型，同时还显示出针对药物靶标对中目标蛋白质和药物分子的关键成分的解释性。

FragXsiteDTI：用Transformer驱动的解读方法揭示药物靶向相互作用中的关键部分