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 atthis https URL.
View on arXiv@article{wang2025_2505.16094,
title={ A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization },
author={ Ziqing Wang and Kexin Zhang and Zihan Zhao and Yibo Wen and Abhishek Pandey and Han Liu and Kaize Ding },
journal={arXiv preprint arXiv:2505.16094},
year={ 2025 }
}