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Related Experiment Video

Updated: May 29, 2026

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

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Precision-Guarded Graph-Text Alignment for Universal Chemical Understanding.

Yongqiu Lin¹, Lian Shen¹, Zhongyu He¹

¹School of Informatics, Xiamen University, Xiamen 361005, China.

Journal of Chemical Information and Modeling

|May 28, 2026

Summary

This summary is machine-generated.

Large Language Models (LLMs) can now achieve high chemical validity and precision in scientific discovery. Our Deep Graph-Text Alignment (DGTA) framework unifies structure generation and numerical property prediction.

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Area of Science:

Artificial Intelligence
Computational Chemistry
Materials Science

Background:

Large Language Models (LLMs) show promise in scientific discovery but struggle with semantic-structure misalignment, producing chemically invalid outputs.
Current multimodal approaches often use naive projection layers, leading to feature collapse and loss of topological information during mixed-precision training.

Purpose of the Study:

To introduce Deep Graph-Text Alignment (DGTA), a precision-first framework for unified structural generation and regression.
To enhance the accuracy and reliability of LLMs in scientific applications, particularly in chemistry and materials science.

Main Methods:

Developed a precision-first framework, Deep Graph-Text Alignment (DGTA).
Introduced a Stability-Optimized Graph Tokenizer with Float32 Precision Guards and LayerNorm Constraints.
Employed a unified approach for structural generation and regression tasks.

Main Results:

Achieved state-of-the-art regression on QM9 with a Mean Absolute Error (MAE) of 0.0068.
Attained 79.6% Average AUC on MoleculeNet for broad classification tasks.
Reduced material design error by 59% (MAE 75.53 to 30.83) and achieved 93.16% structural validity on MolQA.

Conclusions:

DGTA demonstrates universality across quantum precision, broad classification, and generative robustness.
The framework effectively unifies structural generation and regression, overcoming limitations of previous multimodal adaptations.
DGTA significantly improves the chemical validity and precision of LLM-generated scientific data.