t-SMILES 2: Hierarchical Structure Enhances the Generalizability of Linear Molecular Representation
<|reference_start|>t-SMILES 2: Hierarchical Structure Enhances the Generalizability of Linear Molecular Representation: Encoding is the carrier of information. Artificial intelligence models possess basic capabilities in syntax, semantics, and reasoning, but these capabilities are sensitive to specific inputs. This study introduces TSIS (Simplified TSID) to the t-SMILES family, with the intention of conducting a more comprehensive and in-depth evaluation of t-SMILES. TSID has been demonstrated significantly outperforms classical SMILES, DeepSMILES, and SELFIES in previous research. Further analysis of this study reveals that the tree structure utilized by the t-SMILES framework is more effectively comprehensible than initially anticipated. Additionally, TSIS, along with their variants, demonstrate comparable performance to TSID and markedly surpass that of SMILES, SAFE, and SELFIES. Moreover, its format is more straightforward to read. Overall, the contrast analysis indicates that the hierarchical structure of t-SMILES enhances its generalizability. Concurrently, the evaluation of the generative models reveals that the GPT model exhibits the highest novelty-similarity scores. The VAE and diffusion models demonstrate robust capabilities in terms of interpolation, whereas the LSTM model encounters some challenges in parsing complex structures.<|reference_end|>
@article{wu2024t-smiles,
    title={t-SMILES 2: Hierarchical Structure Enhances the Generalizability of
  Linear Molecular Representation},
    author={Juan-Ni Wu, Tong Wang, Li-Juan Tang, Hai-Long Wu, Ru-Qin Yu},
    journal={arXiv preprint arXiv:2402.02164},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02164},
    primaryClass={cs.AI q-bio.BM}
}