Deep learning for retention time prediction in reversed-phase liquid chromatography
Deep learning for retention time prediction in reversed-phase liquid chromatography
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DOI：
10.1016/j.chroma.2021.462792
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发表时间：
2022-01-06
影响因子：
4.1
通讯作者：
Buryak, Aleksey K.
中科院分区：
文献类型：
--
作者：
Fedorova, Elizaveta S.;Matyushin, Dmitriy D.;Buryak, Aleksey K.
高效液相色谱 (HPLC) 中的保留时间预测是许多研究的主题，因为它可以使用 HPLC 与高分辨率质谱联用提高非目标分析中未知分子的识别。考虑到各种分子特性和机器学习算法，开发了许多方法来预测液相色谱中不同数量分子的保留时间。最近从代谢物和化学实体数据库 (METLIN) 中建立的标准化合物的大型保留时间数据集使研究人员能够创建一个模型，可用于预测具有多种结构和理化性质的小分子的保留时间。针对不同的深度学习模型架构，研究了使用最大数据集预测保留时间的能力，这些模型是在分子指纹和表示为单热矩阵的 SMILES（分子的字符串表示）上进行训练的。最好的结果是通过使用 SMILES 作为输入的一维卷积神经网络 (1D CNN) 实现的。该模型的平均绝对误差和中值绝对误差分别为 34.7 秒和 18.7 秒，优于之前针对该数据集获得的结果。将在 METLIN SMRT 数据集上预训练的一维 CNN 转移到其他五个数据集上，以评估泛化能力。 (c) 2021 Elsevier B.V. 保留所有权利。
Retention time prediction in high-performance liquid chromatography (HPLC) is the subject of many studies since it can improve the identification of unknown molecules in untargeted profiling using HPLC coupled with high-resolution mass spectrometry. Lots of approaches were developed for retention time prediction in liquid chromatography for a different number of molecules considering various molecular properties and machine learning algorithms. The recently built large retention time data set of standard compounds from the Metabolite and Chemical Entity Database (METLIN) allows researchers to create a model that can be used for retention time prediction of small molecules with wide varieties of structures and physicochemical properties. The ability to predict retention times using the largest data set was studied for different architectures of deep learning models that were trained on molecular fingerprints, and SMILES (string representation of a molecule) represented as one-hot matrices. The best result was achieved with a one-dimensional convolutional neural network (1D CNN) that uses SMILES as an input. The proposed model reached the mean absolute error and the median absolute error equal to 34.7 and 18.7 s, respectively, which outperformed the results previously obtained for this data set. The pre trained 1D CNN on the METLIN SMRT data set was transferred on five other data sets to evaluate the generalization ability. (c) 2021 Elsevier B.V. All rights reserved.