mlsanityData to 14 Oct 2026

A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization

arXiv 2505.16094 ↗First posted 22 May 2025cs.LG cs.CLcited by 13

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Abstract (arXiv)

Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic notations, with emerging extensions to incorporate multi-modal inputs. To advance the new field of LLM for molecular discovery, this survey provides an up-to-date and forward-looking review of the emerging use of LLMs for two central tasks: molecule generation and molecule optimization. Based on our proposed taxonomy for both problems, we analyze representative techniques in each category, highlighting how LLM capabilities are leveraged across different learning settings. In addition, we include the commonly used datasets and evaluation protocols. We conclude by discussing key challenges and future directions, positioning this survey as a resource for researchers working at the intersection of LLMs and molecular science. A continuously updated reading list is available at https://github.com/REAL-Lab-NU/Awesome-LLM-Centric-Molecular-Discovery.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 59 of its 130 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

59 of 130 references.

References · 59

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38 more
  1. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 364
  2. 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 297
  3. 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 275
  4. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 264
  5. 2018Fr'echet ChemNet Distance: A metric for generative models for molecules in drug discoverycited by 194
  6. 2025Reasoning Models Can Be Effective Without Thinkingcited by 187
  7. 2020MoFlow: An Invertible Flow Model for Generating Molecular Graphscited by 185
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  12. 2023Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspectivecited by 92
  13. 2024MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypothesescited by 72
  14. 2020The Synthesizability of Molecules Proposed by Generative Modelscited by 68
  15. 2023DrugAssist: A Large Language Model for Molecule Optimizationcited by 59
  16. 2024ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedbackcited by 33
  17. 2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
  18. 2024Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planningcited by 29
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  32. 2024Deep Lead Optimization: Leveraging Generative AI for Structural Modificationcited by 10
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Citing papers per year: 2025: 4; 2026: 9.

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  1. 2025Rethinking Entropy Interventions in RLVR: An Entropy Change Perspectivecited by 52
  2. 2025A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solvingcited by 11
  3. 2026MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimizationcited by 4
  4. 2026CoAct: Co-Active LLM Preference Learning with Human-AI Synergycited by 3
  5. 2025Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMscited by 2
  6. 2026GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Modelscited by 1
  7. 2026Closed-Loop Molecular Inverse Design with Semantic LLM Surrogatescited by 1
  8. 2025Assay2Mol: large language model-based drug design using BioAssay contextcited by 0
  9. 2026ES-Merging: Biological MLLM Merging via Embedding Space Signalscited by 0
  10. 2026ReContraster: Making Your Posters Stand Out with Regional Contrastcited by 0
  11. 2026HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classificationcited by 0
  12. 2026M3OS: A Monte Carlo Graph Search-Orchestrated Multi-Agent LLM System for Evidence-Traced Molecular Optimizationcited by 0
1 more
  1. 2026Autonomous Science Bench: Evaluating AI Agents for Autonomous Scientific Discoverycited by 0