mlsanityData to 14 Oct 2026

Group SELFIES: A Robust Fragment-Based Molecular String Representation

arXiv 2211.13322 ↗First posted 23 Nov 2022cs.LG cs.NE physics.chem-phcited by 33

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Abstract (arXiv)

We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustness guarantees. Molecular string representations, such as SMILES and SELFIES, serve as the basis for molecular generation and optimization in chemical language models, deep generative models, and evolutionary methods. While SMILES and SELFIES leverage atomic representations, Group SELFIES builds on top of the chemical robustness guarantees of SELFIES by enabling group tokens, thereby creating additional flexibility to the representation. Moreover, the group tokens in Group SELFIES can take advantage of inductive biases of molecular fragments that capture meaningful chemical motifs. The advantages of capturing chemical motifs and flexibility are demonstrated in our experiments, which show that Group SELFIES improves distribution learning of common molecular datasets. Further experiments also show that random sampling of Group SELFIES strings improves the quality of generated molecules compared to regular SELFIES strings. Our open-source implementation of Group SELFIES is available online, which we hope will aid future research in molecular generation and optimization.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 23 Nov 2022.

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17 of 56 references.

References · 17

  1. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
  2. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 652
  3. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
  4. 2020ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Predictioncited by 337
  5. 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 275
  6. 2018Fr'echet ChemNet Distance: A metric for generative models for molecules in drug discoverycited by 194
  7. 2018Constrained Graph Variational Autoencoders for Molecule Designcited by 185
  8. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
  9. 2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
  10. 2021Keeping it Simple: Language Models can learn Complex Molecular Distributionscited by 68
  11. 2020Cell Complex Neural Networkscited by 67
  12. 2019Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Spacecited by 66
5 more
  1. 2020BOSS: Bayesian Optimization over String Spacescited by 54
  2. 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 32
  3. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  4. 2022Scalable Fragment-Based 3D Molecular Design with Reinforcement Learningcited by 11
  5. 2022FastFlows: Flow-Based Models for Molecular Graph Generationcited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 11; 2025: 3; 2026: 10.

33 papers.

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  1. 2023Gotta be SAFE: A New Framework for Molecular Designcited by 48
  2. 2024ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedbackcited by 33
  3. 2023Searching for High-Value Molecules Using Reinforcement Learning and Transformerscited by 18
  4. 2023Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Designcited by 17
  5. 2023t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generationcited by 14
  6. 2023GPT-MolBERTa: GPT Molecular Features Language Model for molecular property predictioncited by 14
  7. 2023SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Designcited by 13
  8. 2024GP-MoLFormer: A Foundation Model For Molecular Generationcited by 11
  9. 2024Less can be more for predicting properties with large language modelscited by 11
  10. 2023Recent advances in the Self-Referencing Embedding Strings (SELFIES) librarycited by 8
  11. 2023Molecule Design by Latent Prompt Transformercited by 8
  12. 2025mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Moleculescited by 8
21 more
  1. 2024Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generationcited by 7
  2. 2024GraphXForm: Graph transformer for computer-aided molecular designcited by 6
  3. 2025How to Make Large Language Models Generate 100% Valid Molecules?cited by 4
  4. 2024Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Modelscited by 3
  5. 2023Computational modeling of semantic changecited by 2
  6. 2024Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representationcited by 1
  7. 2024Polyatomic Complexes: A topologically-informed learning representation for atomistic systemscited by 1
  8. 2026TSSR: Two-Stage Swap-Reward-Driven Reinforcement Learning for Character-Level SMILES Generationcited by 1
  9. 2026A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Eracited by 1
  10. 2026Toward Better Geometric Representations for Molecule Generative Modelscited by 1
  11. 2024Molecule Design by Latent Prompt Transformercited by 0
  12. 2024Fragment-Masked Diffusion for Molecular Optimizationcited by 0
  13. 2024Exploiting Latent Linearity in LLMs Improves Explainable Molecular Representation Learningcited by 0
  14. 2025ChemFixer: Correcting Invalid Molecules to Unlock Previously Unseen Chemical Spacecited by 0
  15. 2026From Tokens to Blocks: A Block-Diffusion Perspective on Molecular Generationcited by 0
  16. 2026Generative Chemical Language Models for Energetic Materials Discoverycited by 0
  17. 2026Agentic Molecular Recovery via Molecule-Aware Explorationcited by 0
  18. 2026Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmarkcited by 0
  19. 2026Molecular LLM Agents: From Architectural Design to Scientific Autonomycited by 0
  20. 2026Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistrycited by 0
  21. 2026STAMP as a topology-aware molecular language for fragment-level learning and editingcited by 0