aiqbench analysis
This research introduces MolLangData, a massive dataset pairing 163k molecules with precise natural language descriptions generated through an automated rule-based framework. By training a 4B-parameter LLM on this data, the authors achieve high-fidelity molecule reconstruction from text, directly advancing chemical reasoning capabilities within large language models. The work establishes a critical foundation for aligning structural chemistry with linguistic intent, moving beyond incidental mentions of science into core AI application development.
MolLangData: A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method
arXiv:2602.02320v5 Announce Type: replace 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 both understand structure for chemical reasoning and generate molecules from natural-language design intent. However, the substantial cost of human annotation makes it infeasible to construct large-scale, high-quality datasets of structure-grounded descriptions. This work proposes a fully automated annotation framework for generating precise molecular descriptions at scale, such that the original molecule can be unambiguously reconstructed from the description alone. Our approach extends a rule-based chemical nomenclature parser to interpret IUPAC names and construct enriched, XML metadata that explicitly encodes molecular structure. This is then used to guide LLMs in producing accurate natural-language descriptions. Using this framework, we curate MolLangData, a dataset of approximately $163$k molecule--description pairs. A rigorous validation protocol combining expert human and LLM-based reconstruction on a subset of $2,000$ molecules demonstrates $98.6\%$ description precision. Using the curated dataset, we train a $4$B-parameter LLM via large-scale reinforcement learning for language-conditional molecule generation. The proposed framework and dataset provide a reliable foundation for molecule--language alignment, readily beneficial to broader chemical tasks.
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 both understand structure for chemical reasoning and generate molecules from natural-language design intent. However, the substantial cost of human annotation makes it infeasible to construct large-scale, high-quality datasets of structure-grounded descriptions. This work proposes a fully automated annotation framework for generating precise molecular descriptions at scale, such that the original molecule can be unambiguously reconstructed from the description alone. Our approach extends a rule-based chemical nomenclature parser to interpret IUPAC names and construct enriched, XML metadata that explicitly encodes molecular structure. This is then used to guide LLMs in producing accurate natural-language descriptions. Using this framework, we curate MolLangData, a dataset of approximately $163$k molecule--description pairs. A rigorous validation protocol combining expert human and LLM-based reconstruction on a subset of $2,000$ molecules demonstrates $98.6\%$ description precision. Using the curated dataset, we train a $4$B-parameter LLM via large-scale reinforcement learning for language-conditional molecule generation. The proposed framework and dataset provide a reliable foundation for molecule--language alignment, readily beneficial to broader chemical tasks.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
arXiv:2602.02320 [cs.CL]
(or arXiv:2602.02320v5 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2602.02320 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 283 words
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