Several domain-specific optimizations could be explored to further improve the compression ratio of SMILES-based datasets using ZSMILES. One potential optimization is the refinement of the dictionary generation process to identify and prioritize high-frequency patterns more effectively. By fine-tuning the algorithm to focus on the most common and impactful patterns in SMILES representations, the compression ratio can be enhanced.
Another optimization could involve dynamic dictionary adaptation based on the input dataset characteristics. By analyzing the specific features and structures of the molecules in the dataset, ZSMILES could dynamically adjust the dictionary to better capture and represent the data, leading to improved compression efficiency.
Additionally, exploring advanced data preprocessing techniques to enhance pattern recognition and redundancy elimination could contribute to further compression improvements. By incorporating sophisticated data preprocessing algorithms that optimize the input data for better pattern matching, ZSMILES can achieve higher compression ratios while maintaining data integrity and readability.
Overall, continuous research and experimentation in domain-specific optimizations tailored to the unique characteristics of SMILES data can unlock new possibilities for enhancing the compression capabilities of ZSMILES and improving its performance in virtual screening applications.