Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation
arXiv:2412.14642v4 Announce Type: replace Abstract: Recently, Large Language Models (LLMs) have demonstrated great potential in natural language-driven molecule discovery. However, existing datasets and benchmarks for molecule-text alignment are predominantly built on one-to-one mappings, measuring LLMs' ability to retrieve a single, pre-defined answer, rather than their creative potential to generate diverse, yet equally valid, molecular candidates. To address this critical gap, we propose Speak-to-Structure (S^2-Bench), the first benchmark to evaluate LLMs in open-domain natural language-dri
The rapid advancements in LLMs and their application across scientific domains, particularly materials discovery, are driving new evaluation methodologies.
Improving LLMs' ability for open-domain molecule generation will accelerate drug discovery, materials science, and synthetic biology, creating new economic opportunities.
This benchmark shifts the evaluation of LLMs in molecule generation from simple retrieval to creative, diverse output, reflecting a more advanced capability.
- · Pharmaceutical R&D
- · Biotechnology companies
- · AI model developers
- · Materials science
- · Traditional drug discovery methods
- · Manual molecular design chemists
LLMs will become more effective at proposing novel molecular structures for specific applications.
This improved capability could lead to faster development of new drugs, catalysts, and advanced materials.
The acceleration of material and drug discovery may shorten product development cycles and significantly reduce R&D costs across industries.
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Read at arXiv cs.CL