Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models

• URL: http://arxiv.org/abs/2409.15370v1
• Date: Thu, 19 Sep 2024 02:36:04 GMT
• Title: Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models
• Authors: Alexius Wadell, Anoushka Bhutani, Venkatasubramanian Viswanathan,
• Abstract summary: We systematically evaluate thirteen chemistry-specific tokenizers for their coverage of the SMILES language. We introduce two new tokenizers, i>smirk/i> and i>smirk-gpe/i>, which can represent the entirety of the OpenSMILES specification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular Foundation Models are emerging as powerful tools for accelerating molecular design, material science, and cheminformatics, leveraging transformer architectures to speed up the discovery of new materials and drugs while reducing the computational cost of traditional ab initio methods. However, current models are constrained by closed-vocabulary tokenizers that fail to capture the full diversity of molecular structures. In this work, we systematically evaluate thirteen chemistry-specific tokenizers for their coverage of the SMILES language, uncovering substantial gaps. Using N-gram language models, we accessed the impact of tokenizer choice on model performance and quantified the information loss of unknown tokens. We introduce two new tokenizers, <i>smirk</i> and <i>smirk-gpe</i>, which can represent the entirety of the OpenSMILES specification while avoiding the pitfalls of existing tokenizers. Our work highlights the importance of open-vocabulary modeling for molecular foundation models and the need for chemically diverse benchmarks for cheminformatics.

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