In our research, we employed a deep learning model for predicting the strength of Tau interactions with small molecules and for filtering target molecules from the USPTO-50k dataset, drawing inspiration from the open-source work, DeepDTA. To adapt the model to our needs, we retrained it using the PBDbind dataset. We transformed the inputs, represented as SMILES, into vectors using dictionaries and utilized embedding vectors to project these vectors into a higher-dimensional space. This preprocessing step optimized the input data for ligands, enhancing computational efficiency.For processing sequence data, we followed a similar approach. In our virtual screening model, we constructed a DeepDTA network model, incorporating a convolutional neural network (Conv1d), a linear layer, and a ReLU activation function. The preprocessed ligand SMILES and receptor data served as inputs, processed in batches. To minimize the error between predicted and true values, we employed the Mean Squared Error (MSE) loss function. The Adam optimizer was utilized to fine-tune the model parameters, reducing errors and enhancing model fit. During the training process, we set a learning rate of 0.001, ran 600 epochs, and processed 32 samples per batch. These parameters were carefully chosen to facilitate gradual learning of input data characteristics and to incrementally improve prediction accuracy. Through diligent training and optimization, we successfully developed a regression model tailored for ligand-receptor interactions.