ArticleChemical communications (Cambridge, England)2025
Enhancing deep chemical reaction prediction with advanced chirality and fragment representation.
Article in Chemical communications (Cambridge, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This work focuses on organic reaction prediction with deep learning, with the recently introduced fragSMILES representation - which encodes molecular substructures and chirality, enabling compact and expressive molecular representation in a textual form. In a systematic comparison with well-established molecular notations - simplified molecular input line entry system (SMILES), self-referencing embedded strings (SELFIES), sequential attachment-based fragment embedding (SAFE) and tree-based SMILES (t-SMILES) - fragSMILES achieved the highest performance across forward- and retro-synthesis prediction, with superior recognition of stereochemical reaction information. Moreover, fragSMILES enhances the capacity to capture stereochemical complexity - a key challenge in synthesis planning. Our results demonstrate that chirality-aware and fragment-level representations can advance current computer-assisted synthesis planning efforts.
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