Explicitness in SMILES representation via ExACT: improved tokenization for aqueous solubility prediction.
5 affiliations
- 1University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia. [email protected].
- 2Modalis Ltd., Zagreb, Croatia. [email protected].
- 3University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia.
- 4Modalis Ltd., Zagreb, Croatia.
- 5University of Zagreb Faculty of Chemical Engineering and Technology, Zagreb, Croatia.
Abstract
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 contributionThis 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.
Related
- AqSol-Blend: A CPU-Trained Descriptor Ensemble for Aqueous Solubility Prediction
- Hybrid-CatBoost: an empirical comparison of molecular representations for aqueous-solubility prediction on the TDC benchmark
- DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction
- Solubility prediction for ill-defined polymers: A critical comparison of Hansen parameters and COSMO-RS backfitting
- When Do Quantum-Chemical Descriptors Improve Aqueous Solubility Prediction? A Residual-Rescue Benchmark under Chemical Distribution Shift
BibTeX
@article{begusic2026explicitness,
title = {{Explicitness in SMILES representation via ExACT: improved tokenization for aqueous solubility prediction.}},
author = {Domagoj Begušić and Damir Pintar and Zlatko Smole and Ernest Meštrović},
journal = {Journal of cheminformatics},
year = {2026},
month = aug,
doi = {10.1186/s13321-026-01263-w},
url = {https://europepmc.org/article/MED/42958368},
note = {CC BY},
}
RIS
TY - JOUR
TI - Explicitness in SMILES representation via ExACT: improved tokenization for aqueous solubility prediction.
AU - Domagoj Begušić
AU - Damir Pintar
AU - Zlatko Smole
AU - Ernest Meštrović
PY - 2026
DA - 2026/08/04
JO - Journal of cheminformatics
DO - 10.1186/s13321-026-01263-w
UR - https://europepmc.org/article/MED/42958368
AB - 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 contributionThis 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.
SN - 1758-2946
ER -