# Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation

arXiv: https://arxiv.org/abs/1905.13741 · HTML: https://mlsanity.com/paper/1905.13741/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 23 of 23)

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- [Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction](https://mlsanity.com/paper/2309.01788/) (2023)
- [SOLD: SELFIES-based Objective-driven Latent Diffusion](https://mlsanity.com/paper/2509.25198/) (2025)
- [Scaffold-Conditioned Preference Triplets for Controllable Molecular Optimization with Large Language Models](https://mlsanity.com/paper/2604.12350/) (2026)
- [CoRe-Gen: Robust Spectrum-to-Structure Generation under Imperfect Fingerprint Conditions](https://mlsanity.com/paper/2605.12980/) (2026)
- [Atomistic Language Models Understand and Generate Materials](https://mlsanity.com/paper/2606.21395/) (2026)
- [Reward Transport: Property Control in Flow Matching via Noise-Space Alignment](https://mlsanity.com/paper/2607.08781/) (2026)
- [Molecular LLM Agents: From Architectural Design to Scientific Autonomy](https://mlsanity.com/paper/2608.23104/) (2026)

## Abstract (arXiv)

The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering -- generally denoted as inverse design -- was based massively on human intuition and high-throughput virtual screening. The last few years have seen the emergence of significant interest in computer-inspired designs based on evolutionary or deep learning methods. The major challenge here is that the standard strings molecular representation SMILES shows substantial weaknesses in that task because large fractions of strings do not correspond to valid molecules. Here, we solve this problem at a fundamental level and introduce SELFIES (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100% robust. Every SELFIES string corresponds to a valid molecule, and SELFIES can represent every molecule. SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid. In our experiments, the model's internal memory stores two orders of magnitude more diverse molecules than a similar test with SMILES. Furthermore, as all molecules are valid, it allows for explanation and interpretation of the internal working of the generative models.