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首页> 外文期刊>Analytica chimica acta >Quantitative structure-property relationship studies for collision cross sections of 579 singly protonated peptides based on a novel descriptor as molecular graph fingerprint (MoGF)
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Quantitative structure-property relationship studies for collision cross sections of 579 singly protonated peptides based on a novel descriptor as molecular graph fingerprint (MoGF)

机译：基于分子描述符指纹图谱（MoGF）的新型描述符，对579个质子化肽段的碰撞截面进行定量构效关系研究

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摘要

Aiming at ion mobility spectrometry (IMS), computer-assisted ion mobility prediction (CAIMP) has been recently developed to simulate and predict diverse IMS behaviors in assistance of mathematics and computer science. Of that, quantitative structure-property relationship (QSPR) plays a vital role, dedicating to predict properties ofunknown samples by creating statistical model based on known samples. In QSPR, the key lies in how to transform structural characteristics of target compounds into a group of numerical codes. In consideration that future IMS applications may mainly focus on intricate drug/biological systems, a novel molecular structural characterization method referring to molecular graphic fingerprint (MoGF) is proposed in this paper. In MoGF approach, radical distribution function is employed to map intrinsic interatomic correlations into a coordinate system according to a reasonable sampling interval, thus forming the characteristic graph curve which is rich in information on molecular structural characteristics, possessing of great merits in easy calculation, independent of experiments, large information contents, explicit structural meanings and intuitive expressions, etc. Consequently, MoGF is utilized to QSPR studies on 579 singly protonated peptide collision cross sections, and the constructed partial least square (PLS) regression model is confirmed to be robust and predictable by rigorous both internal and external validations, with statistics as r(2) = 0.991, q(2) = 0.990, RMSEE = 5.526, RMSCV = 5.572, q(ext)(2) = 0.990, r(ext)(2) = 0.990, r(0.ext)(2) = 0.990, r '(2)(0.ext) = 0.990, k = 1.003, k ' = 0.996 and RMSEP = 5.561, respectively. (C) 2007 Elsevier B.V. All rights reserved.
机译：针对离子迁移谱（IMS），最近开发了计算机辅助离子迁移率预测（CAIMP），以在数学和计算机科学的辅助下模拟和预测各种IMS行为。其中，定量结构与性质的关系（QSPR）起着至关重要的作用，它通过基于已知样本创建统计模型来预测未知样本的特性。在QSPR中，关键在于如何将目标化合物的结构特征转化为一组数字代码。考虑到未来的IMS应用可能主要集中在复杂的药物/生物系统上，本文提出了一种新的分子结构表征方法，即分子图形指纹（MoGF）。在MoGF方法中，采用自由基分布函数根据合理的采样间隔将内在的原子间相关性映射到坐标系中，从而形成了丰富的分子结构特征信息的特征图曲线，具有易于计算，独立的优点。因此，将MoGF应用于579个质子化肽段碰撞截面的QSPR研究，并证实所构建的偏最小二乘（PLS）回归模型具有鲁棒性和可通过严格的内部和外部验证来预测，统计量为r（2）= 0.991，q（2）= 0.990，RMSEE = 5.526，RMSCV = 5.572，q（ext）（2）= 0.990，r（ext）（2 ）= 0.990，r（0.ext）（2）= 0.990，r'（2）（0.ext）= 0.990，k = 1.003，k'= 0.996和RMSEP = 5.561。 （C）2007 Elsevier B.V.保留所有权利。

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