  Encoding is the carrier of information. AI models possess basic capabilities
in syntax, semantics, and reasoning, but these capabilities are sensitive to
specific inputs. In this study, we introduce an encoding algorithm, TSIS
(Simplified TSID), to the t-SMILES family as a fragment-based linear molecular
representation. TSID has been demonstrated to significantly outperform
classical SMILES, DeepSMILES, and SELFIES in previous work. A further
comparative analysis in this study reveals that the tree structure used by TSID
is more easily learned than anticipated, regardless of whether Transformer or
LSTM models are used. Furthermore, TSIS demonstrates comparable performance to
TSID and significantly outperforms SMILES, SELFIES, and SAFE. While SEFLIES and
SAFE present significant challenges in semantic and syntactic analysis,
respectively, due to their inherent complexity.
