ArticleBioinformatics (Oxford, England)2024
ChatMol: interactive molecular discovery with natural language.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A survey of contrastive learning methods in molecular representation.Briefings in bioinformatics · 2026Review
- Multimodal pre-training models of molecular representation for drug discovery.National science review · 2026Review
- Large Language Models in Genomics-A Perspective on Personalized Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Bioinformatics and biomedical informatics with ChatGPT: Year one review.Quantitative biology (Beijing, China) · 2024Article
- DrugAssist: a large language model for molecule optimization.Briefings in bioinformatics · 2024Article
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10 authors.
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Abstract
motivationNatural language is poised to become a key medium for human-machine interactions in the era of large language models. In the field of biochemistry, tasks such as property prediction and molecule mining are critically important yet technically challenging. Bridging molecular expressions in natural language and chemical language can significantly enhance the interpretability and ease of these tasks. Moreover, it can integrate chemical knowledge from various sources, leading to a deeper understanding of molecules.
resultsRecognizing these advantages, we introduce the concept of conversational molecular design, a novel task that utilizes natural language to describe and edit target molecules. To better accomplish this task, we develop ChatMol, a knowledgeable and versatile generative pretrained model. This model is enhanced by incorporating experimental property information, molecular spatial knowledge, and the associations between natural and chemical languages. Several typical solutions including large language models (e.g. ChatGPT) are evaluated, proving the challenge of conversational molecular design and the effectiveness of our knowledge enhancement approach. Case observations and analysis offer insights and directions for further exploration of natural-language interaction in molecular discovery. AVAILABILITY AND IMPLEMENTATION: Codes and data are provided in https://github.com/Ellenzzn/ChatMol/tree/main.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.