DPTM-DT：用于药物-靶点预测的双预训练Transformer多任务表示学习

中文摘要 AI 辅助

药物-靶点关系预测支持候选药物筛选、药物重定位和机制分析。现有模型常使用不完整的药物或蛋白质表示，对跨模态交互建模较浅，或将亲和力回归与交互分类分开训练，尽管这些任务描述了同一药物-靶点对的密切相关的视图。本文提出DPTM-DT，一个用于多任务药物-靶点预测的双预训练Transformer框架。DPTM-DT结合GROVER分子图嵌入、ESM蛋白质语言模型嵌入和CTD理化描述符，然后通过双向跨模态注意力交换药物-靶点信息。共享的配对表示用于连续亲和力回归、高亲和力二分类和六级亲和力分类。在Davis和KIBA上的实验涵盖随机80/20划分和DeepDTA风格的标准划分。在随机80/20划分上，DPTM-DT在Davis上达到MSE/CI值0.193/0.917，在KIBA上达到0.120/0.918。它还报告了在Davis和KIBA上的二分类AUPR/MCC值分别为0.727/0.654和0.798/0.689，以及六分类Macro-F1/Top-2值分别为0.800/0.932和0.815/0.962。在报告的回归、二分类和多分类设置中，DPTM-DT在比较方法中取得了最佳整体性能。标准划分下的结果显示出相同的相对趋势。消融实验表明，双目标表示、门控融合和跨模态注意力各自对最终性能有所贡献。代码和补充材料可在以下网址获取：此HTTP URL。

英文摘要

Drug-target relation prediction supports candidate screening, drug repositioning, and mechanism analysis. Existing models often use incomplete drug or protein representations, model cross-modal interactions shallowly, or train affinity regression and interaction classification separately, although these tasks describe closely related views of the same drug-target pair. This paper presents DPTM-DT, a dual-pretrained Transformer framework for multitask drug-target prediction. DPTM-DT combines GROVER molecular graph embeddings, ESM protein language-model embeddings, and CTD physicochemical descriptors, then exchanges drug-target information through bidirectional cross-modal attention. A shared pair representation is used for continuous affinity regression, high-affinity binary classification, and six-level affinity classification. Experiments on Davis and KIBA cover random 80/20 and DeepDTA-style standard splits. On the random 80/20 split, DPTM-DT achieves MSE/CI values of 0.193/0.917 on Davis and 0.120/0.918 on KIBA. It also reports binary AUPR/MCC values of 0.727/0.654 and 0.798/0.689, and six-class Macro-F1/Top-2 values of 0.800/0.932 and 0.815/0.962 on Davis and KIBA, respectively. Across the reported regression, binary classification, and multiclass classification settings, DPTM-DT achieves the best overall performance among the compared methods. Results under the standard split show the same relative trend. Ablations indicate that dual target representation, gated fusion, and cross-modal attention each contribute to the final performance. Code and supplementary materials are available at: anonymous.4open.science/r/DPCM-DT-74E0.

发表机构

• Beihang University（北京航空航天大学）

机构由 AI 辅助整理，请以论文原文为准。