Drug target binding affinity (DTA) is a key criterion for drug screening. Existing experimental methods are time-consuming and rely on limited structural and domain information. While learning-based methods can model sequence and structural information, they struggle to integrate contextual data and often lack comprehensive modeling of drug-target interactions. In this study, we propose a novel DTA prediction method, termed HGTDP-DTA, which utilizes dynamic prompts within a hybrid Graph-Transformer framework. Our method generates context-specific prompts for each drug-target pair, enhancing the model's ability to capture unique interactions. The introduction of prompt tuning further optimizes the prediction process by filtering out irrelevant noise and emphasizing task-relevant information, dynamically adjusting the input features of the molecular graph. The proposed hybrid Graph-Transformer architecture combines structural information from Graph Convolutional Networks (GCNs) with sequence information captured by Transformers, facilitating the interaction between global and local information. Additionally, we adopted the multi-view feature fusion method to project molecular graph views and affinity subgraph views into a common feature space, effectively combining structural and contextual information. Experiments on two widely used public datasets, Davis and KIBA, show that HGTDP-DTA outperforms state-of-the-art DTA prediction methods in both prediction performance and generalization ability.
预测化合物和靶标之间的相互作用对于快速药物再利用至关重要。深度学习已成功应用于药物-靶标亲和力（DTA）问题。然而，以前基于深度学习的方法忽略了对药物和蛋白质残基之间的直接相互作用进行建模。这将导致目标表示的不准确学习，其可能由于药物结合效应而改变。此外，以前的DTA方法仅基于DTA数据集中的少量蛋白质序列来学习蛋白质表示，而忽略了DTA数据集之外的蛋白质的使用。我们提出了GEFA（Graph Early Fusion Affinity），一种新的带有注意力机制的图中图神经网络，以解决由于绑定效应而引起的目标表征的变化。具体地说，药物被建模为原子图，然后作为一个节点在一个更大的图的残留物-药物复合物。最后得到的模型是一个具有表达能力的深度嵌套图神经网络。我们还使用了由最近学习情境化蛋白质表示的努力提供支持的预训练蛋白质表示。这些实验在不同的环境下进行，以评估新药或靶点等场景。结果表明，预训练的蛋白质嵌入的有效性和我们的GEFA在建模药物-靶标相互作用的嵌套图中的优势。
ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion
School of Software Engineering Huazhong University of Science；School of Cyber Science；Engineering Huazhong University of Science；Union Hospital, Tongji Medical College Huazhong University of Science
HiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction
School of Software Engineering Huazhong University of Science（软件工程学院 华中科技大学）；Faculty of Data Science City University of Macau Macau, China（数据科学学院 澳门城市大学）；Union Hospital, Tongji Medical College Huazhong University of Science（同济医学院附属医院 华中科技大学）
Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors
Gaoling School of AI (GSAI), Renmin University of China（中国人民大学高瓴人工智能学院（GSAI））；Microsoft Research（微软研究院）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；University of Science and Technology of China（中国科学技术大学）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education（教育部下一代智能搜索与推荐工程研究中心）
Associative Learning Mechanism for Drug-Target Interaction Prediction
Chongqing University of Posts and Telecommunications（重庆邮电大学）；State University of New York at Buffalo State（纽约州立大学布法罗州立学院）；DH (Shanghai) Diagnostics Co.,Ltd.（DH（上海）诊断有限公司）
Co-Diffusion: An Affinity-Aware Two-Stage Latent Diffusion Framework for Generalizable Drug-Target Affinity Prediction
School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Westlake University（西湖大学）；School of Artificial Intelligence, Beihang University（北航人工智能学院）；Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University（辽宁省生物资源研究开发重点实验室，东北大学生命与健康科学学院）
