化学
谱线
人工神经网络
紫外线
动态时间归整
吸光度
模式识别(心理学)
生物系统
紫外可见光谱
均方误差
人工智能
试验装置
分析化学(期刊)
计算机科学
统计
色谱法
光学
数学
物理
有机化学
生物
天文
作者
Fabio Urbina,Kushal Batra,Kevin J Luebke,J. A. White,Daniel Matsiev,Lori L. Olson,Jeremiah P. Malerich,Maggie A. Z. Hupcey,Peter B. Madrid,Sean Ekins
标识
DOI:10.1021/acs.analchem.1c03741
摘要
Ultraviolet-visible (UV-Vis) absorption spectra are routinely collected as part of high-performance liquid chromatography (HPLC) analysis systems and can be used to identify chemical reaction products by comparison to the reference spectra. Here, we present UV-adVISor as a new computational tool for predicting the UV-Vis spectra from a molecule's structure alone. UV-Vis prediction was approached as a sequence-to-sequence problem. We utilized Long-Short Term Memory and attention-based neural networks with Extended Connectivity Fingerprint Diameter 6 or molecule SMILES to generate predictive models for the UV spectra. We have produced two spectrum datasets (dataset I, N = 949, and dataset II, N = 2222) using different compound collections and spectrum acquisition methods to train, validate, and test our models. We evaluated the prediction accuracy of the complete spectra by the correspondence of wavelengths of absorbance maxima and with a series of statistical measures (the best test set median model parameters are in parentheses for model II), including RMSE (0.064), R2 (0.71), and dynamic time warping (DTW, 0.194) of the entire spectrum curve. Scrambling molecule structures with the experimental spectra during training resulted in a degraded R2, confirming the utility of the approaches for prediction. UV-adVISor is able to provide fast and accurate predictions for libraries of compounds.
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