面向无约束红外分子结构解析的数据融合与对比对齐

中文摘要 AI 辅助

近年来，从红外（IR）光谱数据进行自动化分子结构解析取得了显著进展，但其广泛适用性因依赖作为辅助模型输入的预先确定的化学式而受到限制，这将模型预测限制为异构体识别而非完整分子结构预测。尽管Transformer模型已被证明能以高准确率识别分子异构体，但其在无约束结构解析中的可靠性相对较低且研究尚不充分。本工作对传统的编码器-解码器Transformer提出并评估了关键改进：为更好地解决无约束问题的巨大化学空间，我们实现了一种新颖的混合专家（MoE）解码器模块，该模块利用基于线性序统计量和Choquet积分的非加性聚合；我们还修改了Transformer，使其在聚合光谱表示时也使用这些非加性算子。结合辅助对比对齐损失项，这些改进使Top-K预测准确率相比仅使用红外数据的基线模型提升了超过10个百分点。通过对分子预测结果的子结构片段分析，我们进一步确认红外光谱编码了绝大多数相关化学信息，这表明异构体排序模型的更高性能在很大程度上是由于所探索化学空间中分子的吸收带代表性不足或重叠。最终，本工作通过证明从实测红外光谱进行自动化分子结构解析的有效性，显著拓宽了AI在分析化学中的应用范围。

英文摘要

Automated molecular structure elucidation from infrared (IR) spectroscopy data has seen significant advancements in recent years, but its broad applicability is limited by a reliance on pre-determined chemical formulas provided as auxiliary model inputs. This limits model predictions to isomer identification rather than full molecular structure prediction. Although transformer models have been shown to identify molecular isomers with high accuracy, their reliability for unconstrained structure elucidation is comparatively low and poorly understood. In this work, we propose and evaluate key modifications to the traditional encoder-decoder transformer. To better address the vast chemical space of the unconstrained problem, we implement a novel Mixture-of-Experts (MoE) decoder module that utilizes non-additive aggregation via linear-order statistics and the Choquet integral. We further modify the transformer to utilize these non-additive operators when aggregating spectral representations as well. Together with an auxiliary contrastive alignment loss term, these enhancements improve Top-K prediction accuracy by over 10 percentage points compared to baseline IR-only models. Through sub-structure fragment analysis of molecular predictions, we further confirm that infrared spectra encode the vast majority of relevant chemical information, implying that the higher performance of isomer-ranking models is largely due to underrepresented or overlapping absorption bands for molecules in the explored chemical space. Ultimately, by demonstrating the efficacy of automated molecular structure elucidation from measured IR spectra, this work serves to significantly broaden the utility of AI in analytical chemistry.

发表机构

• University of Missouri（密苏里大学）

机构由 AI 辅助整理，请以论文原文为准。