A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method
arXiv:2602.02320v4 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure. Accurately aligning molecular structure with natural language is therefore essential for enabling large language models (LLMs) to reason about downstream chemical tasks. However, the substantial cost of human annotation makes it infeasible to construct large-scale, high-quality datasets of structure-grounded descriptions. In this work, we propose a fully automated annotation framework for generating precise molecular descriptions that preserve complete structural details at scale. O
The increasing sophistication of LLMs and the recognition of their potential in scientific domains are driving efforts to overcome data annotation bottlenecks for specialized applications like molecular structure understanding.
Accurate and scalable alignment of molecular structure with natural language is critical for enabling AI systems to perform complex reasoning and accelerate discovery in chemistry and biology.
The proposed automated annotation framework could significantly reduce the cost and time associated with creating large, high-quality datasets for training LLMs on molecular tasks, accelerating drug discovery and materials science.
- · AI/ML researchers
- · Pharmaceutical industry
- · Biotechnology sector
- · Computational chemists
- · Manual data annotation services (specialized chemical data)
Rapid expansion of large language models capable of understanding and generating molecular descriptions.
Accelerated drug discovery pipelines and development of novel materials through AI-driven design.
Democratization of complex chemical synthesis and biological engineering via AI-powered platforms.
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Read at arXiv cs.AI