# Drug-Target Affinity Prediction Based on Cross-Modal Fusion of Text and Graph
## 1 Introduction  
Currently, the development of a new drug takes nearly 20 years to obtain approval from the U.S. Food and Drug Administration (FDA) and costs approximately USD 260 million. In the field of drug-target affinity prediction, various wet lab methods, including Surface Plasmon Resonance (SPR [1]), Isothermal Titration Calorimetry (ITC [2]), and Enzyme-Linked Immunosorbent Assay (ELISA [3]), have been widely used. However, the high initial investment costs and complex technical requirements have been persistent barriers to technological advancement in this field. Drug-target affinity prediction methods can currently be categorized into three types: those based on traditional experimental methods, those based on computational methods, and those based on deep learning. Figure 1 provides an overview of the representative methods. With the development of computer technology, data-driven in silico methods have achieved significant success across various drug discovery fields and have clearly become an important tool [4]. For instance, In silico Medicine, a leading biotech company, has successfully applied deep learning models in the field of drug discovery. Its designed drug, INS018_055, advanced to clinical trials in just 18 months, significantly reducing the timeline by nearly three to four years compared to the traditional drug discovery process, which typically takes about five years. Against the backdrop of deep learning and big data, computer-based drug-target affinity prediction methods have effectively improved the efficiency of financial and human resource utilization. Since the introduction of the SimBoost model by [PERSON] et al. [5] in 2017, the concept of drug-target affinity (DTA) prediction has garnered significant attention. SimBoost demonstrated outstanding performance in computing prediction intervals and assessing affinity confidence. However, its limitation lies in the underutilization of deep learning's potential. With technological advancements, [PERSON] et al. [6] introduced DeepDTA in 2018, the first deep learning model combining convolutional neural networks (CNNs) and fully connected neural networks for DTA prediction. Although DeepDTA exhibited high configurability and predictive performance across multiple datasets, it lacked a deep exploration of structural features. To address this issue, researchers proposed WideDTA [7], which significantly enriched the original input data by incorporating protein domain features, motifs, and maximum common substructure word features. However, WideDTA's design focused solely on intra-textual modality fusion, employing a simple weighted summation of extracted textual features. This approach failed to explore or effectively capture the mutual guidance between drug and target textual features and other representational modalities, limiting its performance in DTA prediction. In 2019, [PERSON] et al. [8] introduced AttentionDTA, pioneering the use of attention mechanisms for efficient feature interaction. While this model improved prediction accuracy by emphasizing relationships between important features, its computational complexity posed a significant challenge when handling large-scale datasets. In 2020, [PERSON] et al. [9] advanced the field with GraphDTA, which shifted from traditional drug textual sequence modeling to extracting information from molecular graphs. This innovation reduced the mean squared error (MSE) from 0.282 to 0.254. Despite its success in improving prediction performance, GraphDTA's aggregation strategy remained limited to first-order neighborhood information, failing to fully exploit higher-order neighborhood features. Consequently, the model struggled to capture complex molecular structural relationships. Building on the success of GraphDTA, [PERSON] et al. [10] proposed DGraphDTA, which replaced amino acid sequences with protein contact graphs and developed a dual-branch graph-based model, further enhancing representational capabilities.