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多壁碳纳米管致突变潜力的类定量构效关系研究。

Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes.

机构信息

IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy.

IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy.

出版信息

Chemosphere. 2015 Apr;124:40-6. doi: 10.1016/j.chemosphere.2014.10.067. Epub 2014 Nov 20.

Abstract

Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set).

摘要

CORAL 软件(http://www.insilico.eu/coral)可在互联网上使用,用于建立预测多壁碳纳米管(MWCNTs)致突变潜力的准定量构效关系(quasi-QSAR)。与之前基于简化分子输入线输入系统(SMILES)表示分子结构的 CORAL 模型不同,基于浓度、存在(不存在)S9 混合物、使用(或不使用)预孵育等条件表示的准 QSAR 由所谓的准 SMILES 编码。这些模型(准 QSAR)的三个随机拆分的统计特征为可见训练集和测试集以及不可见验证集如下:(i)拆分 1:n=13,r(2)=0.8037,q(2)=0.7260,s=0.033,F=45(训练集);n=5,r(2)=0.9102,s=0.071(测试集);n=6,r(2)=0.7627,s=0.044(验证集);(ii)拆分 2:n=13,r(2)=0.6446,q(2)=0.4733,s=0.045,F=20(训练集);n=5,r(2)=0.6785,s=0.054(测试集);n=6,r(2)=0.9593,s=0.032(验证集);(iii)n=14,r(2)=0.8087,q(2)=0.6975,s=0.026,F=51(训练集);n=5,r(2)=0.9453,s=0.074(测试集);n=5,r(2)=0.8951,s=0.052(验证集)。

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