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Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

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Improving the Reliability of Molecular String Representations for Generative Chemistry.

Etienne Reboul^1,2, Zoe Wefers², Harish Prabakaran¹

¹Université Paris Cité, CNRS, Laboratoire de Biochimie Théorique, Paris 75005, France.

Journal of Chemical Information and Modeling

|September 17, 2025

Summary

This summary is machine-generated.

Choosing the right molecular representation is key for generative chemistry models. ClearSMILES, a new method, significantly improves the validity and accuracy of generated molecules compared to SMILES and SELFIES.

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Area of Science:

Computational chemistry
Machine learning in drug discovery

Background:

Generative modeling in chemistry is rapidly advancing.
SMILES is a common but flawed molecular representation for generative tasks.
SELFIES ensures molecule validity but can lack fidelity.

Purpose of the Study:

To comprehensively evaluate SMILES and SELFIES for generative models.
To assess molecular generation viability and fidelity.
To develop improved data augmentation strategies.

Main Methods:

Evaluation of SMILES and SELFIES using viability and fidelity metrics.
Development of data augmentation procedures for both representations.
Introduction of ClearSMILES, a stochastic augmentation method for SMILES.

Main Results:

RDKit canonical SMILES generated invalid molecules 20% of the time.
SELFIES generated valid molecules but with low fidelity to training data.
ClearSMILES reduced invalid samples to 2.2% and improved fidelity.

Conclusions:

Neither SMILES nor SELFIES are optimal for generative molecular modeling alone.
ClearSMILES enhances SMILES, offering a more robust representation.
Data augmentation is crucial for improving molecular generation quality.