Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme.

To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is DF4nanoGrouping scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property 'lowest unoccupied molecular orbital') need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low- one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the DF4nanoGrouping scheme that has been proposed before.

Modeling adsorption of brominated, chlorinated and mixed bromo/chloro-dibenzo-p-dioxins on C60 fullerene using Nano-QSPR.

Many technological implementations in the field of nanotechnology have involved carbon nanomaterials, including fullerenes such as the buckminsterfullerene, C60. The unprecedented properties of such organic nanomaterials (in particular their large surface area) gained extensive attention for their potential use as organic pollutant sorbents. Sorption interactions can be very hazardous and useful at the same time. This work investigates the influence of halogenation by bromine and/or chlorine in dibenzo-p-dioxins on their sorption ability on the C60 fullerene surface. Halogenated dibenzo-p-dioxins (PXDDs, where X = Br or Cl) are ever-present in the environment and accidently produced in many technological processes in only approximately known quantities. If all combinatorial Br and/or Cl dioxin substitution possibilities are present in the environment, the experimental characterization and investigation of sorbent effectiveness is more than difficult. In this work, we have developed a quantitative structure-property relationship (QSPR) model (R2 = 0.998), predicting the adsorption energy [kcal/mol] for 1,701 PXDDs adsorbed on C60 (PXDD@C60). Based on the QSPR model reported herein, we concluded that the lowest energy PXDD@C60 complexes are those that the World Health Organization (WHO) considers to be less dangerous with respect to the aryl hydrocarbon receptor (AhR) toxicity mechanism. Therefore, the effectiveness of fullerenes as sorbent agents may be underestimated as sorption could be less effective for toxic congeners than previously believed.

ILPC: simple chemometric tool supporting the design of ionic liquids.

BACKGROUND: Ionic liquids (ILs) found a variety of applications in today's chemistry. Since their properties depend on the ions constituting particular ionic liquid, it is possible to synthetize IL with desired specification, dependently on its further function. However, this task is not trivial, since knowledge regarding the influence of particular ion on the property of concern is crucial. Therefore, there is a strong need for new, fast and inexpensive methods supporting the process of ionic liquids' design, making it possible to predefine IL's properties even before the synthesis. RESULTS: We have developed a simple tool (called Ionic Liquid PhysicoChemical predictor: ILPC) that allows for the simultaneous qualitative prediction of four physicochemical properties of ionic liquids: viscosity, n-octanol-water partition coefficient, solubility and enthalpy of fusion. By the means of Principal Component Analysis, we studied 172 ILs and defined distribution trends of those four properties, dependently on the ILs structures. We proved that the qualitative prediction of mentioned properties could be performed on the basis of most simple information we can deliver about ILs, which are their molecular formulas. CONCLUSIONS: Created tool presented in this paper allows fast, pre-synthesis screening of ILs, with the omission of any experimental steps. It can be helpful in the process of designing ILs with preferred properties. We proved that the information encrypted in molecular formula of ionic liquid could be a valuable source of knowledge regarding the IL's viscosity, n-octanol-water partition coefficient, solubility and enthalpy of fusion. Moreover, we proved that the influence of both ions, constituting the IL, on each of those four properties indicates same, additive trend.Graphical AbstractSchematic representation of ILPC performance - the exact position of the ionic liquid on the linear map is determined by its chemical structure.

Combinatorial × computational × cheminformatics (C3) approach to characterization of congeneric libraries of organic pollutants.

Congeners are molecules based on the same carbon skeleton but are different by the number of substituents and/or a substitution pattern. Examples are 1-chloronaphthalene, 1,4-dichloronaphthalene, and 1,3,8-trichloronaphthalene. Various persistent organic pollutants (POPs) exist in the environment as families of congeners. Very large numbers of possible congeners make their experimental characterization and risk assessment unfeasible. Computational high-throughput and quantitative structure-property relationship (QSPR) modeling has been limited by the lack of tools and approaches facilitating analysis of such POP families. We present a comprehensive approach that enables modeling of extremely large congeneric libraries. The approach involves three steps: (1) combinatorial generation of a library of congeners, (2) quantum chemical characterization of each structure at the PM6 semiempirical level to obtain molecular descriptors, and (3) analysis of the information generated in step 2. In steps 1-3, we employ combinatorial, computational, and cheminformatics techniques, respectively. Therefore, this hybrid approach is named "Combinatorial × Computational × Cheminformatics", or just abbreviated as C(3) (or C-cubed) approach. We demonstrate the usefulness of this approach by generating and characterizing Br- and Cl-substituted congeneric families of 23 typical POPs. The analysis of the resulting set of 1 840 951 congeners that includes Cl-, Br-, and mixed Br/Cl-substituted species, proves that, based on structural similarities defined by the molecular descriptors' values, the existing QSPR models developed originally for Cl- and Br-substituted congeners can be applied also to mixed Br/Cl-substituted ones. Thus, the C(3) approach may serve as a tool for exploring structural applicability domains of the existing QSPR models for congeneric sets.

Advancing risk assessment of engineered nanomaterials: application of computational approaches.

Nanotechnology that develops novel materials at size of 100nm or less has become one of the most promising areas of human endeavor. Because of their intrinsic properties, nanoparticles are commonly employed in electronics, photovoltaic, catalysis, environmental and space engineering, cosmetic industry and - finally - in medicine and pharmacy. In that sense, nanotechnology creates great opportunities for the progress of modern medicine. However, recent studies have shown evident toxicity of some nanoparticles to living organisms (toxicity), and their potentially negative impact on environmental ecosystems (ecotoxicity). Lack of available data and low adequacy of experimental protocols prevent comprehensive risk assessment. The purpose of this review is to present the current state of knowledge related to the risks of the engineered nanoparticles and to assess the potential of efficient expansion and development of new approaches, which are offered by application of theoretical and computational methods, applicable for evaluation of nanomaterials.