QSAR/QSPR在POPs归趋与风险评价中的应用

持久性有机污染物(POPs)是目前备受国际社会关注的高危害性有机污染物,对它们的环境归趋分析和风险评价需要获得大量可靠的性质数据和毒性数据,而定量结构活性/性质相关(QSAR/QSPR)方法为快速有效地获得这些数据提供了可能性。QSAR/QSPR模型已在预测POPs的生物活性/性质,补充缺失的基础数据及探求POPs的环境过程机制和生态效应机理等方面得到了广泛应用,近年来也在新POPs物质的筛选、归趋模拟以及风险评价等方面有着更进一步的应用或潜在应用前景。本文介绍了QSAR/QSPR在POPs性质和生物活性预测中的基本应用及其在POPs归趋和风险评价中的扩展应用,并对QSAR/QSPR在POPs研究领域的应用前景进行了展望。     

定量结构-性质/活性关系在分析和环境化学中的进展及应用

定量结构-性质/活性关系(QSPR/QSAR)是目前国际上一个比较活跃的研究领域.该方法将理论计算方法和各种统计分析工具相结合,研究系列化合物的结构与其生物活性和各种物理化学性质之间的定量函数关系.QSPR/QSAR不仅可以建立预测化合物的各种理化性质以及生物活性的理论模型,而且可以发现和确定对化合物的各种性质起决定作...     

Open computing grid for molecular science and engineering

Grid is an emerging infrastructure for distributed computing that provides secure and scalable mechanisms for discovering and accessing remote software and data resources. Applications built on this infrastructure have great potential for addressing and solving large scale chemical, pharmaceutical, and material science problems. The article describes the concept behind grid computing and will present the OpenMolGRID system that is an open computing grid for molecular science and engineering. This system provides grid enabled components, such as a data warehouse for chemical data, software for building QSPR/QSAR models, and molecular engineering tools for generating compounds with predefined chemical properties or biological activities. The article also provides an overview about the availability of chemical applications in the grid.     

Liquid-crystal nanoscience: an emerging avenue of soft self-assembly

Liquid crystals are finding increasing applications in a wide variety of fields including liquid-crystal display technology, materials science, bioscience, etc., apart from acting as prototype self-organizable supramolecular soft materials and tunable solvents. Recently, keeping in pace with topical science, liquid crystals have entered into the fascinating domains of nanoscience and nanotechnology. This tutorial review describes the recent and significant developments in liquid-crystal nanoscience embracing contemporary nanomaterials such as nanoparticles, nanorods, nanotubes, nanoplatelets, etc. The dispersion of zero-, one- and two-dimensional nanomaterials in liquid crystals for the enhancement of properties, liquid-crystalline phase behavior of nanomaterials themselves, self-assembly and alignment of nanomaterials in liquid-crystalline media, and the synthesis of nanomaterials by using liquid crystals as 'templates' or 'precursors' have been highlighted and discussed. It is almost certain that the 'fourth state of matter' will play more prevalent roles in nanoscience and nanotechnology in the near future. Moreover, liquid-crystal nanoscience reflects itself as a beautiful demonstration of the contemporary theme "crossing the borders: science without boundaries".     

表面活性剂的QSAR/QSPR研究进展*

本文综述了表面活性剂定量结构-活性/性质相关(QSAR/QSPR)研究的最新进展,以及相关结构描述符在表面活性剂QSAR/QSPR研究中的应用.重点介绍了表面活性剂的cmc和表面张力γ与分子结构的定量关系、离子型表面活性剂电荷分布的定量计算及其对胶体结构与性质的影响.对表面活性剂分子结构与活性/性质的定量相关研究的发展趋势进行了展望.     

QSAR and QSPR studies of a highly structured physicochemical domain

The relevance of terms other than linear when deriving quantitative structure-activity relationship/quantitative structure-property relationship (QSAR/QSPR) models has been rarely considered so far. In this study, the impact of quadratic and interacting terms has been taken into account. The first effect of including such highly structured terms is a significant extension of the parametric domain that moves from the initial N to N(N + 3)/2 parameters. This substantial enlargement over the conventional linear boundaries involves a higher computational cost due to the increased combinatorial number of resulting theoretical QSAR/QSPR models. To face this issue, novel genetic-algorithm-based software, MGZ (multigenetic zooming), was developed and used for both variable selection and model building. To speed up the entire process of domain searching, MGZ was supported with multiple independent evolving populations and genetic storms to further QSAR/QSPR analyses. In addition, a novel fitness function was developed to score models on the basis of their inner predictive capability, assessed on the training set, structure complexity, and presence of nonlinear terms. The models were further validated by monitoring model redundancy and performing intensive randomization runs. The Selwood data set was used as a reference set to derive QSAR models. Furthermore, a QSPR study was conducted on the solubility data set of a large array of organic compounds. The results reported in the present paper demonstrate that our approach is successful in finding linear models, which are at least as good as the models previously derived using standard statistical approaches, and in deriving new nonlinear models with good statistical figures.     

Nanotechnology: Emerging Tool for Diagnostics and Therapeutics

Nanotechnology is an emerging technology which is an amalgamation of different aspects of science and technology that includes disciplines such as electrical engineering, mechanical engineering, biology, physics, chemistry, and material science. It has potential in the fields of information and communication technology, biotechnology, and medicinal technology. It involves manipulating the dimensions of nanoparticles at an atomic scale to make use of its physical and chemical properties. All these properties are responsible for the wide application of nanoparticles in the field of human health care. Promising new technologies based on nanotechnology are being utilized to improve diverse aspects of medical treatments like diagnostics, imaging, and gene and drug delivery. This review summarizes the most promising nanomaterials and their application in human health.     

定量结构-活性/性质相关性中变量的选择——正交变换法与最优子集回归法的比较

定量结构－活性／性质相关性（QSAR／QSPR）研究的基本依据是化合物的性质与结构具有相关性,所以只要有方法描述化合物的结构（得到X）就可与化合物的性质（作为Y）建立起数学模型,并由引模型预测未知化合物。由化合物的结构可衍生（即描述）出诸多变量,从统计学出发,希望用尽可能少的变量来表征尽可能多的信息（如多元回归分析）。过多的变量不仅计算量大,从而可以导致所得的数学模型不稳定,使预测结果较差^[1],而且不同变量的组合所得结果可能差别很大,由此需要对变量进行压缩和选择。虽然变量的选择是一个非常费时和复杂的工作,但变量选择的好坏对数学模型的稳定性及准确性有致关重要的影响,从某种角度上讲,它能决定一项QSAR／QSPR研究的成败。最简单的选择变量的方法是穷举组合法,但此方法的计算量非常大,特别是当变量数较大时,该方法是实际上是不可行的,尽管用于变量选择的方法已有报道,但问题尚有待进一步研究。本文侧重比较了正交变换法与变量最优子集回归法,得到了很有启示性的结果。     

Nature-inspired biogenic synthesis of silver nanoparticles for antibacterial applications

In recent decades, nanotechnology has been empowered as a new and developing interdisciplinary region of science and innovation that coordinates material science and biology. Nanoscience and nanotechnology open up new streets of examination that are helpful in synthesizing novel nanomaterials with remarkable applications. Among different metal nanomaterials, silver nanoparticles (AgNPs) attracted the attention of researchers due to their versatile antibacterial characteristics and biological properties. Biogenically synthesizing AgNPs from plants and microorganisms seems to be a highly promising alternative for developing a technology that is both environmentally benign and fast. Plants and microorganisms' ability to synthesize AgNPs has mostly remained untapped, and the lack of investigation is due to the vast variety of plants and microorganisms. This review aims to describe the current progress in various synthetic techniques for AgNPs and their potential for antibacterial applications. It discusses biogenic synthetic approaches, the role of various metabolites in the growth processes of AgNPs with antibacterial implications, bactericidal mechanisms, and the influence of operational parameters on AgNPs synthesis. Furthermore, the present status, critical challenges, and future outlook of AgNPs will be explored, which will definitely affect their present and future scenarios. We believe that by focusing readers' attention on nature-inspired, biogenically synthesized AgNPs and their bactericidal applications, this review will enable them to formulate a new perspective.     

Modeling based on subspace orthogonal projections for QSAR and QSPR research

A novel projection modeling method for quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) is developed in this paper. Orthogonalization of block variables is introduced to deal with the problem of variable selection. Projections based on least squares are used to construct the modeling space in order to search for the best regression directions for chemical modeling. A suitable prediction space for such a model is further defined to confine the usage range of the model. Three real data sets were analyzed to check the performance of the proposed modeling method. The results obtained from Monte‐Carlo cross‐validation (MCCV) showed that the proposed modeling method might provide better results for QSAR and QSPR modeling than PCR and PLS with respect to both fitting and prediction abilities. Copyright © 2007 John Wiley & Sons, Ltd.     

Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .     

Novel atomic-level-based AI topological descriptors: application to QSPR/QSAR modeling

Novel atomic level AI topological indexes based on the adjacency matrix and distance matrix of a graph is used to code the structural environment of each atomic type in a molecule. These AI indexes, along with Xu index, are successfully extended to compounds with heteroatoms in terms of novel vertex degree v(m), which is derived from the valence connectivity delta(v) of Kier-Hall to resolve the differentiation of heteroatoms in molecular graphs. The multiple linear regression (MLR) is used to develop the structure-property/activity models based on the modified Xu and AI indices. The efficiency of these indices is verified by high quality QSPR/QSAR models obtained for several representative physical properties and biological activities of several data sets of alcohols with a wide range of non-hydrogen atoms. The results indicate that the physical properties studied are dominated by molecular size, but other atomic types or groups have small influences dependent on the studied properties. Among all atomic types, -OH groups seem to be most important due to hydrogen-bonding interactions. On the contrary, -OH groups play a dominant role in biological activities studied, although molecular size is also an important factor. These results indicate that both Xu and AI indices are useful model parameters for QSPR/QSAR analysis of complex compounds.     

On the topological sub-structural molecular design (TOSS-MODE) in QSPR/QSAR and drug design research

A recently introduced graph-theoretical approach to the study of structure-property-activity relationships is presented. The theoretical approach and the computational strategy for the use of the TOSS-MODE approach are given with details. Several QSPR and QSAR applications are reviewed including the study of physical properties of organic compounds, diamagnetic susceptibilities, and biological properties. The applications of the TOSS-MODE approach to discrimination of active/inactive compounds, the virtual screening of compounds with a desired property from databases of chemical structures, identification of active/inactive fragments and its relationships with 2D/3D pharmacophores, and to the design of novel compounds with desired biological activities are also reviewed.     

Stable isotope labeling of nanomaterials for biosafety evaluation and drug development

Quantitative information, such as environmental migration, absorption, biodistribution, biotransformation, and elimination, is fundamental and essential for the nanosafety evaluations of nanomaterials. Due to the complexity of biological and environmental systems, it is challenging to develop quantitative approaches and tools that could characterize intrinsic behaviors of nanomaterials in the organisms. The isotopic tracers are ideal candidates to tune the physical properties of nanomaterials while preserving their chemical properties. In this review article, we summarized the stable isotope labeling methods of nanomaterials for evaluating their environmental and biological effects. The skeleton labeling protocols of carbon nanomaterials and metal/metal oxide nanoparticles were introduced. The advantages and disadvantages of stable isotope labeling were discussed in comparison with other quantitative methods for nanomaterials. The quantitative information of nanomaterials in environmental and biological systems was summarized along with the biosafety data. The benefits for drug development of nanomedicine were analyzed based on the targeting effects, persistent accumulation, and safety. Finally, the challenges and future perspectives of stable isotope labeling in nanoscience and nanotechnology were discussed.     

Intrinsic therapeutic applications of noble metal nanoparticles: past, present and future

Biomedical nanotechnology is an evolving field having enormous potential to positively impact the health care system. Important biomedical applications of nanotechnology that may have potential clinical applications include targeted drug delivery, detection/diagnosis and imaging. Basic understanding of how nanomaterials, the building blocks of nanotechnology, interact with the cells and their biological consequences are beginning to evolve. Noble metal nanoparticles such as gold, silver and platinum are particularly interesting due to their size and shape dependent unique optoelectronic properties. These noble metal nanoparticles, particularly of gold, have elicited a lot of interest for important biomedical applications because of their ease of synthesis, characterization and surface functionalization. Furthermore, recent investigations are demonstrating another promising application of these nanomaterials as self-therapeutics. To realize the potential promise of these unique inorganic nanomaterials for future clinical translation, it is of utmost importance to understand a few critical parameters; (i) how these nanomaterials interact with the cells at the molecular level; (ii) how their biodistribution and pharmacokinetics influenced by their surface and routes of administration; (iii) mechanism of their detoxification and clearance and (iv) their therapeutic efficacy in appropriate disease model. Thus in this critical review, we will discuss the various clinical applications of gold, silver and platinum nanoparticles with relevance to above parameters. We will also mention various routes of synthesis of these noble metal nanoparticles. However, before we discuss present research, we will also look into the past. We need to understand the discoveries made before us in order to further our knowledge and technological development (318 references).     

Health impact and safety of engineered nanomaterials

Many engineered nanomaterials (NMs) are being synthesized and explored for potential use in consumer and medical products. Already, nanoparticles (NPs) of titanium dioxide (TiO(2)), zinc oxide (ZnO), silver (Ag) and other metals or their oxides are present in commercial products such as sunscreens, cosmetics, wound dressings, surgical tools, detergents, automotive paints and tires. More recent and advanced FDA-approved use of NMs includes quantum dots (QDs) in live cell imaging, zirconium oxides in bone replacement and prosthetic devices and nanocarriers in drug delivery. The benefits from nanotechnology are aplenty, comprising antimicrobial activities, scratch- and water-resistance, long-lasting shine, improved processor speeds and better display resolution, to name a few. While developers of these products often focus on the exciting beneficial aspects of their products, safety and toxicity issues are often not discussed in detail. Long-term effects such as chronic exposure and environmental pollution are even less documented. Along with widespread manufacture and use of NMs, concerns for occupational hazards, proper handling, disposal, storage, shipping and clean up are expected to rise. This review focus on the possible biological impact of engineered NPs, serving as a reminder that nanomaterials can become a double-edged sword if not properly handled.