By utilizing techniques such as Natural Language Processing (NLP) and graph-based methods, we extract features to facilitate the prediction of drug-target interaction. For instance, through Word2Vec and Graph Convolutional Network (GCN), we learn effective representations of protein and drug data in the hidden layers, which serve as inputs to subsequent classifiers.

By employing Multi-Modal learning, we enhance the predictive capabilities of traditional drug-target simulation and integrate multiple diverse features, such as protein sequence data, protein-drug docking data, and drug SMILEs data. These advancements find applications in the field of drug development.