Quantitative nanostructure-activity relationship modeling.
Quantitative nanostructure-activity relationship modeling.
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DOI：
10.1021/nn1013484
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发表时间：
2010-10-26
期刊：
影响因子：
17.1
通讯作者：
Tropsha A
中科院分区：
文献类型：
--
作者：
Fourches D;Pu D;Tassa C;Weissleder R;Shaw SY;Mumper RJ;Tropsha A
对人造纳米颗粒(MNPs)所造成的生物效应的评估，无论是期望的还是不期望的，对于纳米技术来说都是至关重要的。实验研究，特别是毒理学研究，耗时、昂贵，而且往往不切实际，需要开发能够预测MNPs生物效应的有效计算方法。为此，我们研究了化学信息学方法的潜力，如定量结构-活性关系(QSAR)建模，以建立测量的MNPs的生物活性与其物理、化学和几何性质之间的统计显著关系，无论是通过实验测量的还是根据MNPs的结构计算的。为了反映研究的背景，我们将我们的方法命名为定量纳米结构-活性关系(QNAR)建模。我们采用了最近使用体外细胞分析的两组具有代表性的MNPs：(I)51种不同金属核的MNPs(PNAS，2008,105，pp7387-7392)和(Ii)109种核相似但表面修饰剂不同的MNPs(NAT)。Biotechno.，2005，23,1418-1423页)。我们使用了基于支持向量机(SVM)的分类和基于k近邻(KNN)的回归等机器学习方法来生成QNAR模型，分类建模的外部预测能力高达73%，回归建模的R2为0.72。我们的结果表明，QNAR模型可以用于：(I)预测新型纳米材料的生物活性分布，以及(Ii)优先考虑纳米材料的设计和制造，以获得更好和更安全的产品。
Evaluation of biological effects, both desired and undesired, caused by Manufactured NanoParticles (MNPs) is of critical importance for nanotechnology. Experimental studies, especially toxicological, are time-consuming, costly, and often impractical, calling for the development of efficient computational approaches capable of predicting biological effects of MNPs. To this end, we have investigated the potential of cheminformatics methods such as Quantitative Structure – Activity Relationship (QSAR) modeling to establish statistically significant relationships between measured biological activity profiles of MNPs and their physical, chemical, and geometrical properties, either measured experimentally or computed from the structure of MNPs. To reflect the context of the study, we termed our approach Quantitative Nanostructure-Activity Relationship (QNAR) modeling. We have employed two representative sets of MNPs studied recently using in vitro cell-based assays: (i) 51 various MNPs with diverse metal cores (PNAS, 2008, 105, pp 7387–7392) and (ii) 109 MNPs with similar core but diverse surface modifiers (Nat. Biotechnol., 2005, 23, pp 1418–1423). We have generated QNAR models using machine learning approaches such as Support Vector Machine (SVM)-based classification and k Nearest Neighbors (kNN)-based regression; their external prediction power was shown to be as high as 73% for classification modeling and R2 of 0.72 for regression modeling. Our results suggest that QNAR models can be employed for: (i) predicting biological activity profiles of novel nanomaterials, and (ii) prioritizing the design and manufacturing of nanomaterials towards better and safer products.
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影响因子：
4.1
作者：
Liu, Jianzhong;Hopfinger, Anton J.
通讯作者：
Hopfinger, Anton J.
影响因子：
2.8
作者：
Heinemann, M.;Schaefer, H. G.
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通讯作者：
Urban, Pawel L.
影响因子：
5.6
作者：
Fourches D;Muratov E;Tropsha A
通讯作者：
Tropsha A
影响因子：
3.7
作者：
Harhaji, Ljubica;Isakovic, Aleksandra;Trajkovic, Vladimir
通讯作者：
Trajkovic, Vladimir