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Douglas J Klein

Publications and source records attributed to Douglas J Klein.

7 recordsLinked to original sources
PubMed2006

Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quantitative super-structure/activity relationships (QSSAR).

During bioconcentration, chemical pollutants from water are absorbed by aquatic animals via the skin or a respiratory surface, while the entry routes of chemicals during bioaccumulation are both directly from the environment (skin or a respiratory surface) and indirectly from food. The bioconcentration factor (BCF) and the bioaccumulation factor (BAF) for a particular chemical compound are defined as the ratio of the concentration of a chemical inside an organism to the concentration in the surrounding environment. Because the experimental determination of BAF and BCF is time-consuming and expensive, it is efficacious to develop models to provide reliable activity predictions for a large number of chemical compounds. Polychlorinated biphenyls (PCBs) released from industrial activities are persistent pollutants of the environment that produce widespread contamination of water and soil. PCBs can bioaccumulate in the food chain, constituting a potential source of exposure for the general population. To predict the bioconcentration and bioaccumulation factors for PCBs we make use of the biphenyl substitution-reaction network for the sequential substitution of H-atoms by Cl-atoms. Each PCB structure then occurs as a node of this reaction network, which is some sort of super-structure, turning out mathematically to be a partially ordered set (poset). Rather than dealing with the molecular structure via ordinary QSAR we use only this poset, making different quantitative super-structure/activity relationships (QSSAR). Thence we developed cluster expansion and splinoid QSSARs for PCB bioconcentration and bioaccumulation factors. The predictive ability of the BAF and BCF models generated for 20 data sets (representing different conditions and fish species) was evaluated with the leave-one-out cross-validation, which shows that the splinoid QSSAR (r between 0.903 and 0.935) are better than models computed with the cluster expansion (r between 0.745 and 0.887). The splinoid QSSAR models for BAF and BCF yield predictions for the missing PCBs in the investigated data sets.

Animals↗
PubMedDate not supplied

Computing wiener-type indices for virtual combinatorial libraries generated from heteroatom-containing building blocks.

The expensive and time-consuming process of drug lead discovery is significantly accelerated by efficiently screening molecular libraries with a high structural diversity and selecting subsets of molecules according to their similarity toward specific collections of active compounds. To characterize the molecular similarity/diversity or to quantify the drug-like character of compounds the process of screening virtual and synthetic combinatorial libraries uses various classes of structural descriptors, such as structure keys, fingerprints, graph invariants, and various topological indices computed from atomic connectivities or graph distances. In this paper we present efficient algorithms for the computation of several distance-based topological indices of a molecular graph from the distance invariants of its subgraphs. The procedures utilize vertex- and edge-weighted molecular graphs representing organic compounds containing heteroatoms and multiple bonds. These equations offer an effective way to compute for weighted molecular graphs the Wiener index, even/odd Wiener index, and resistance-distance index. The proposed algorithms are especially efficient in computing distance-based structural descriptors in combinatorial libraries without actually generating the compounds, because only distance-based indices of the building blocks are needed to generate the topological indices of any compound assembled from the building blocks.

Journal Article↗
PubMedDate not supplied

Random walks and chemical graph theory.

Simple random walks probabilistically grown step by step on a graph are distinguished from walk enumerations and associated equipoise random walks. Substructure characteristics and graph invariants correspondingly defined for the two types of random walks are then also distinct, though there often are analogous relations. It is noted that the connectivity index as well as some resistance-distance-related invariants make natural appearances among the invariants defined from the simple random walks.

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PubMedDate not supplied

Molecules-in-molecule estimation of the extent of localization of Kekuléan substructures in polycyclic aromatic hydrocarbons.

This article first revises graph-theoretical (local aromaticity and overall molecular) indices, introduced by M. Randić in 1975, for benzenoid hydrocarbons and somewhat improves them for computer enumeration. This goes beyond total Kekulé structure enumeration, yielding an index calculation useful for the quantitative estimation of localization of different Kekuléan substructures (including ethylene-, benzene-, annulene-, and radialene-units). This may be viewed as a "molecules-in molecule" approach to polycyclic aromatic hydrocarbons within the context of graph theoretical partitioning.

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PubMedDate not supplied

Posetic quantitative superstructure/activity relationships (QSSARs) for chlorobenzenes.

As a result of the widespread industrial use of polychlorinated hydrocarbons, they have accumulated in nearly all types of environmental compartments, especially in aquatic systems. Particularly, chloroaromatics are among the most undesirable industrial effluents because of their persistence and toxicity. To predict chlorobenzene (CB) toxicities, we make use of a novel scheme that looks beyond simple molecular structure to the manner in which such a structure embeds in an overall reaction network. Thence, a resultant modeling gives a quantitative superstructure/activity relationship (QSSAR) with the (chloro-substitution) reaction network viewed mathematically as a partially ordered set (or poset). Different numerical fittings to the overall poset lead to different QSSAR models, of which we investigate three: average poset, cluster expansion, and splinoid poset QSSAR models for the CBs' toxicities against various species (Poecilia reticulata,Pimephales promelas, Daphnia magna, Rana japonica, etc). Excellent results are obtained for all QSSAR toxicity models. On the basis of the poset reaction diagram, all three of these QSSAR models reflect, in distinct ways, the topology of the network that describes the interconversion of chemical species. Although in the majority of investigated datasets all poset QSSAR models give very good predictions, in some cases, they complement each other. These differences show that more reliable predictions can be obtained by using a consensus prediction that combines data from the three posetic models.

Chlorobenzenes↗
PubMedDate not supplied

The eight classes of positive-curvature graphitic nanocones.

The eight different classes of single-wall positive-curvature graphitic nanocones are emphasized, characterized, and illustrated. Local transformations among different possible apex-region structures within a common class are investigated, it being noted that there is no short sequence of such transformations which convert from any one of these eight classes to another. Attention is directed to class identification and to nanocone codes, especially for "buckycones" comprised solely from pentagonal and hexagonal rings.

Journal Article↗