# Recent advances in the Self-Referencing Embedding Strings (SELFIES) library

arXiv: https://arxiv.org/abs/2302.03620 · HTML: https://mlsanity.com/paper/2302.03620/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Grammar Variational Autoencoder](https://mlsanity.com/paper/1703.01925/) (2017)
- [Molecular De Novo Design through Deep Reinforcement Learning](https://mlsanity.com/paper/1704.07555/) (2017)
- [Deep Reinforcement Learning for De-Novo Drug Design](https://mlsanity.com/paper/1711.10907/) (2017)
- [SELFIES and the future of molecular string representations](https://mlsanity.com/paper/2204.00056/) (2022)
- [LIMO: Latent Inceptionism for Targeted Molecule Generation](https://mlsanity.com/paper/2206.09010/) (2022)
- [Group SELFIES: A Robust Fragment-Based Molecular String Representation](https://mlsanity.com/paper/2211.13322/) (2022)
- [Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations](https://mlsanity.com/paper/2012.09712/) (2020)
- [Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning](https://mlsanity.com/paper/2012.11293/) (2020)

## Built on by (most cited first, 8 of 8)

- [Explainability Techniques for Chemical Language Models](https://mlsanity.com/paper/2305.16192/) (2023)
- [Tokenization for Molecular Foundation Models](https://mlsanity.com/paper/2409.15370/) (2024)
- [A Genetic Algorithm for Navigating Synthesizable Molecular Spaces](https://mlsanity.com/paper/2509.20719/) (2025)
- [Beyond Affinity: A Benchmark of 1D, 2D, and 3D Methods Reveals Critical Trade-offs in Structure-Based Drug Design](https://mlsanity.com/paper/2601.14283/) (2026)
- [Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS](https://mlsanity.com/paper/2402.08210/) (2024)
- [Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations](https://mlsanity.com/paper/2508.14844/) (2025)
- [TSSR: Two-Stage Swap-Reward-Driven Reinforcement Learning for Character-Level SMILES Generation](https://mlsanity.com/paper/2601.04521/) (2026)
- [General Intelligence-based Fragmentation (GIF): A framework for peak-labeled spectra simulation](https://mlsanity.com/paper/2511.09571/) (2025)

## Abstract (arXiv)

String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines. However, traditional string-based representations such as SMILES are often prone to syntactic and semantic errors when produced by generative models. To address these problems, a novel representation, SELF-referencIng Embedded Strings (SELFIES), was proposed that is inherently 100% robust, alongside an accompanying open-source implementation. Since then, we have generalized SELFIES to support a wider range of molecules and semantic constraints and streamlined its underlying grammar. We have implemented this updated representation in subsequent versions of selfieslib, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of selfieslib (version 2.1.1) in this manuscript.