用Transformer模型快速准确解析有机分子结构，替代传统复杂系统。
A Transformer Based Generative Chemical Language AI Model for Structural Elucidation of Organic Compounds
- 基于Transformer的生成式模型，直接从光谱数据生成分子结构。
- 29个原子以内分子解析仅需几秒，前15名准确率达83%。
- 适合化学、药物研发人员快速获取分子结构信息。
过去半个世纪，有机化合物的计算机辅助结构解析（CASE）系统依赖于复杂的专家系统和显式编程算法。由于需探索并筛选庞大的化学结构空间，这些系统在处理复杂分子时计算效率低下。本研究提出一种基于Transformer的生成式化学语言人工智能模型，作为经典CASE框架的端到端替代方案，实现基于光谱数据的超快、精准结构解析。模型采用编码器-解码器架构与自注意力机制，类似大型语言模型，可直接生成与输入光谱数据最匹配的化学结构。该模型在约10.2万组红外、紫外及¹H NMR光谱数据上训练，可在现代CPU上对含最多29个原子的分子进行结构解析，耗时仅数秒，前15名准确率达到83%。该方法展示了生成式AI在加速传统科学问题解决中的潜力，其基于新数据快速迭代的能力，预示着结构解析领域将迎来快速发展。
原文摘要 · Abstract (English)
For over half a century, computer-aided structural elucidation systems (CASE) for organic compounds have relied on complex expert systems with explicitly programmed algorithms. These systems are often computationally inefficient for complex compounds due to the vast chemical structural space that must be explored and filtered. In this study, we present a proof-of-concept transformer based generative chemical language artificial intelligence (AI) model, an innovative end-to-end architecture designed to replace the logic and workflow of the classic CASE framework for ultra-fast and accurate spectroscopic-based structural elucidation. Our model employs an encoder-decoder architecture and self-attention mechanisms, similar to those in large language models, to directly generate the most probable chemical structures that match the input spectroscopic data. Trained on ~ 102k IR, UV, and 1H NMR spectra, it performs structural elucidation of molecules with up to 29 atoms in just a few seconds on a modern CPU, achieving a top-15 accuracy of 83%. This approach demonstrates the potential of transformer based generative AI to accelerate traditional scientific problem-solving processes. The model's ability to iterate quickly based on new data highlights its potential for rapid advancements in structural elucidation.
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