Hybridization of SMILES and chemical-environment-aware tokens to improve performance of molecular structure generation
Year: 2025
Type: article
Abstract: The Simplified Molecular Input Line Entry System (SMILES) is one of the most widely adopted molecular representations. However, SMILES notation suffers from limited token diversity and a lack of chemical information within individual tokens. To address these limitations while maintaining its simplicity, we propose a molecular representation method through the hybridization of standard SMILES tokens with Atom-In-SMILES (AIS) tokens, which incorporate local chemical environment information into a single token. This hybrid representation, termed SMI + AIS, combines SMILES and AIS tokens, allowing AIS tokens to differentiate chemical elements based on their chemical context without introducing additional tokens for less frequent elements. Using the SMI + AIS representation, we evaluated its performance by comparing the predefined metric of generated structures in chemical structure generation based on latent space optimization. Compared to standard SMILES, SMI + AIS achieved a 7% improvement in binding affinity and a 6% increase in synthesizability, highlighting its utility in the enhancement of machine learning-based molecular design. Our results demonstrate that the SMI + AIS representation provides a more effective and informative approach to encapsulate chemical context and presents potential for performance enhancement in other machine learning tasks in chemistry.
Source: Scientific Reports
Authors: Herim Han, Min Sun Yeom, Sunghwan Choi
Institution: Inha University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G526082676
OpenAlex ID: W4410410407