Enhancing deep chemical reaction prediction with advanced chirality and fragment representation

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.