arXiv:2510.16590v2 Announce Type: replace Abstract: Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised methods. In this work, we introduce a framework for molecular reasoning using general-purpose Large Language Models (LLMs) that operates without requiring task-specific model training. Our method anchors chain-of-thought reasoning to the molecular structure by using unique atomic identifiers. First, the LLM performs a zero-shot task to identify relevant fragments and their associated chemical labels o
The rapid advancements in large language models and their increasing multimodal capabilities are enabling applications in complex scientific domains previously inaccessible to general-purpose AI.
This development indicates a significant leap in AI's ability to reason without vast task-specific datasets, potentially accelerating discovery and innovation in chemistry and related fields.
Traditional reliance on extensive labeled data for chemistry AI is challenged, with LLMs demonstrating an ability to perform molecular reasoning using structural identifiers.
- · AI researchers (LLMs)
- · Pharmaceutical industry
- · Materials science
- · Machine learning in chemistry
- · Traditional supervised learning methods
- · Chemists reliant on manual retrosynthesis
- · Companies with large, inefficient data labeling operations
Molecular design and drug discovery processes become significantly more efficient through AI-driven retrosynthesis.
Accelerated development of new compounds and materials could lead to breakthroughs in various industries, from medicine to manufacturing.
Enhanced AI capability in 'atom-anchored' reasoning could generalize to other scientific domains requiring precise understanding of structural relationships, fostering cross-disciplinary AI agents.
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