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<p>The representation of molecular structure in textual form plays a central role in data-driven cheminformatics, not just for deep learning models that often rely on sequence-based inputs, but also for classic machine learning pipelines which are still relevant in this field. The Simplified Molecular Input Line Entry System (SMILES) representation and its explicit variants differ substantially, yet the implications of this difference for tokenization and downstream property prediction remain insufficiently characterized. This paper systematically investigates the role of explicitness in SMILES representation and its impact on aqueous solubility prediction. Four SMILES variations ranging from basic and canonical to explicit and explicit canonical are reviewed to illustrate how increasing explicit chemical detail alters the available information content. Building on this analysis, a novel tokenization approach termed ExACT (Explicit Atom level Context Tokenization) is introduced, which directly leverages atom level explicitness and local chemical context. In addition, a general strategy for enhancing existing tokenization methods through increased explicitness is proposed and demonstrated by upgrading the current state of the art atom-in-SMILES method to an explicit variant. All approaches are evaluated on the AqSolDB dataset using consistent machine learning pipelines and cross validation protocols. The results show that increased explicitness systematically improves predictive performance across tokenization strategies, yielding higher coefficients of determination, lower mean absolute and root mean squared errors, and reduced variance across folds. The proposed ExACT method achieves the best overall performance, largely outperforming traditional molecular fingerprint representations while remaining statistically comparable to the best competing tokenization methods. Furthermore, the efficiency analysis demonstrated that ExACT offers additional computational advantages, including lower dimensionality and the elimination of the decoding step, resulting in faster feature computation. These findings demonstrate that explicit SMILES representations encode chemically meaningful information that can be effectively exploited through context-aware tokenization, providing an interpretable and computationally efficient alternative representation for molecular property prediction.</p><p><b>Scientific contribution</b></p><p>This work investigates the role of explicitness in SMILES and its impact on aqueous solubility prediction, proposes a novel ExACT tokenization method and a general strategy for enhancing existing tokenization methods through increased explicitness.</p>

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Explicitness in SMILES representation via ExACT: improved tokenization for aqueous solubility prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Domagoj Begušić,
  • Damir Pintar,
  • Zlatko Smole,
  • Ernest Meštrović

摘要

The representation of molecular structure in textual form plays a central role in data-driven cheminformatics, not just for deep learning models that often rely on sequence-based inputs, but also for classic machine learning pipelines which are still relevant in this field. The Simplified Molecular Input Line Entry System (SMILES) representation and its explicit variants differ substantially, yet the implications of this difference for tokenization and downstream property prediction remain insufficiently characterized. This paper systematically investigates the role of explicitness in SMILES representation and its impact on aqueous solubility prediction. Four SMILES variations ranging from basic and canonical to explicit and explicit canonical are reviewed to illustrate how increasing explicit chemical detail alters the available information content. Building on this analysis, a novel tokenization approach termed ExACT (Explicit Atom level Context Tokenization) is introduced, which directly leverages atom level explicitness and local chemical context. In addition, a general strategy for enhancing existing tokenization methods through increased explicitness is proposed and demonstrated by upgrading the current state of the art atom-in-SMILES method to an explicit variant. All approaches are evaluated on the AqSolDB dataset using consistent machine learning pipelines and cross validation protocols. The results show that increased explicitness systematically improves predictive performance across tokenization strategies, yielding higher coefficients of determination, lower mean absolute and root mean squared errors, and reduced variance across folds. The proposed ExACT method achieves the best overall performance, largely outperforming traditional molecular fingerprint representations while remaining statistically comparable to the best competing tokenization methods. Furthermore, the efficiency analysis demonstrated that ExACT offers additional computational advantages, including lower dimensionality and the elimination of the decoding step, resulting in faster feature computation. These findings demonstrate that explicit SMILES representations encode chemically meaningful information that can be effectively exploited through context-aware tokenization, providing an interpretable and computationally efficient alternative representation for molecular property prediction.

Scientific contribution

This work investigates the role of explicitness in SMILES and its impact on aqueous solubility prediction, proposes a novel ExACT tokenization method and a general strategy for enhancing existing tokenization methods through increased explicitness.