G. Lee et al. provided essential information about tau protein and its significance in neurobiology and Alzheimer's disease, which provides a foundation for further research and exploration of this protein's functions and roles in health and disease. To screen candidate targeting small molecules from the database of uspto-50k, we used the open-source DeepDTA model that is developed by Elif Ozkirimli et. al. As shown in Fig. , this method first uses Conv1d to extract features from protein sequences and small molecule SMILES coding, and employs an aggregation network to predict binding energies. Specifically, we retrained the DeepDTA model using the PDBbind dataset. It completed convergence after 300 epochs. We used the model to analyse the affinity energy values between Tau and ligand molecules. These ligand molecules are from uspto-50k datasets. In Fig. , we have represented the distribution of affinity energies of drug molecules in bar degrees. Among them, We selected the molecule that has the largest value of binding energy with Tau. The chemical structure of this molecule was shown in Fig. . We assessed the drug-like properties of molecules via MolWt (molecular weight), BertzCT (molecular complexity index), TPSA (molecular polar surface area), LogP (Wildman-Crippen LogP value71), and NumHAcceptors (number of hydrogen acceptors). These proprties were calculated with RDkit. In subsequent stages, we intend to leverage the trained reinforcement learning model to generate synthetic paths.