Interacting Evolution of Modeling and Data Exchange : A Case for Predicting Chemical-Protein Interactive Visualization

In the process of data exchange, we assert that the needs of data users should be prioritized and clearly articulated for a trustworthy data marketplace. Here, we present a case study on predicting and discovering chemical-protein interactions, which are crucial for drug discovery, by collecting data tailored to these requirements. We developed a linear sequence model based on a list of amino acid residues to meet this need, and the data were selected for analysis with this model. A convolutional neural network is employed to capture the spatial characteristics of proteins, while also integrating the linear molecular features of drugs and the network topology. The framework’s 3D visualization provides intuitive representations of chemical-protein interactions, enhancing biological interpretation. This approach effectively balances data processing efficiency with rich structural insights, enabling a deeper analysis of critical protein-related features.