Results 1  10
of
45
Variable Neighborhood Search
, 1997
"... Variable neighborhood search (VNS) is a recent metaheuristic for solving combinatorial and global optimization problems whose basic idea is systematic change of neighborhood within a local search. In this survey paper we present basic rules of VNS and some of its extensions. Moreover, applications a ..."
Abstract

Cited by 207 (18 self)
 Add to MetaCart
Variable neighborhood search (VNS) is a recent metaheuristic for solving combinatorial and global optimization problems whose basic idea is systematic change of neighborhood within a local search. In this survey paper we present basic rules of VNS and some of its extensions. Moreover, applications are briefly summarized. They comprise heuristic solution of a variety of optimization problems, ways to accelerate exact algorithms and to analyze heuristic solution processes, as well as computerassisted discovery of conjectures in graph theory.
Variable Neighborhood Search for Extremal Graphs 6. Analyzing Bounds for the Connectivity Index
, 2000
"... Recently, Araujo and De la Pe~na [1] gave bounds for the connectivity index of chemical trees as a function of this index for general trees and the ramification index of trees. They also gave bounds for the connectivity index of chemical graphs as a function of this index for maximal subgraphs which ..."
Abstract

Cited by 18 (6 self)
 Add to MetaCart
Recently, Araujo and De la Pe~na [1] gave bounds for the connectivity index of chemical trees as a function of this index for general trees and the ramification index of trees. They also gave bounds for the connectivity index of chemical graphs as a function of this index for maximal subgraphs which are trees and the cyclomatic number of the graphs. The ramification index of a tree is first shown to be equal to the number of pending vertices minus 2. Then, in view of extremal graphs obtained with the system AutoGraphiX, all bounds of Araujo and De la Pe\~na [1] are improved, yielding tight bounds, and in one case corrected. Moreover, chemical trees of given order and number of pending vertices with minimum and with maximum connectivity index are characterized.
An integrated som fuzzy artmap neural system for the evaluation of toxicity
 J. Chem. Inf. Comput. Sci
, 2002
"... Selforganized maps (SOM) have been applied to analyze the similarities of chemical compounds and to select from a given pool of descriptors the smallest and more relevant subset needed to build robust QSAR models based on fuzzy ARTMAP. First, the category maps for each molecular descriptor and for ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
Selforganized maps (SOM) have been applied to analyze the similarities of chemical compounds and to select from a given pool of descriptors the smallest and more relevant subset needed to build robust QSAR models based on fuzzy ARTMAP. First, the category maps for each molecular descriptor and for the target activity variable were created with SOM and then classified on the basis of topology and nonlinear distribution. The best subset of descriptors was obtained by choosing from each cluster the index with the highest correlation with the target variable and then in order of decreasing correlation. This process was terminated when a dissimilarity measure increased, indicating that the inclusion of more molecular indices would not add supplementary information. The optimal subset of descriptors was used as input to a fuzzy ARTMAP architecture modified to effect predictive capabilities. The performance of the integrated SOMfuzzy ARTMAP approach was evaluated with the prediction of the acute toxicity LC50 of a homogeneous set of 69 benzene derivatives in the fathead minnow and the oral rat toxicity LD50 of a heterogeneous set of 155 organic compounds. The proposed methodology minimized the problem of misclassification of similar compounds and significantly enhanced the predictive capabilities of a properly trained fuzzy ARTMAP network.
Use of Statistical and Neural Net Methods in Predicting Toxicity of Chemicals: A Hierarchical QSAR Approach
 Predictive Toxicology of Chemicals: Experiences and Impacts of AI Tools
, 1999
"... A contemporary trend in computational toxicology is the prediction of toxicity endpoints and toxic modes of action of chemicals from parameters that can be calculated directly from their molecular structure. Topological, geometrical, substructural, and quantum chemical parameters fall into this ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
A contemporary trend in computational toxicology is the prediction of toxicity endpoints and toxic modes of action of chemicals from parameters that can be calculated directly from their molecular structure. Topological, geometrical, substructural, and quantum chemical parameters fall into this category. We have been involved in the development of a new hierarchical quantitative structureactivity relationship (QSAR) approach in predicting physicochemical, biomedicinal and toxicological properties of various sets of chemicals. This approach uses increasingly more complex molecular descriptors for model building in a graduated manner. In this paper we will apply statistical and neural net methods in the development of QSAR models for predicting toxicity of chemicals using topostructural, topochemical, geometrical, and quantum chemical indices. The utility and limitations of the approach will be discussed. Introduction In 1998 the number of chemicals registered with the...
The mconnectivity index of graphs
 MATCH Commun. Math. Comput. Chem
"... The mconnectivity index m χα(G) of an organic molecule whose molecular graph is G is the sum of the weights (di1 di2...dim+1)α, where i1 −i2 −...−im+1 runs over all paths of length m in G and di denotes the degree of vertex vi. We find upper bounds for m χα(G) when m ≥ 1 and α ≥ −1 (α � = 0) using ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
The mconnectivity index m χα(G) of an organic molecule whose molecular graph is G is the sum of the weights (di1 di2...dim+1)α, where i1 −i2 −...−im+1 runs over all paths of length m in G and di denotes the degree of vertex vi. We find upper bounds for m χα(G) when m ≥ 1 and α ≥ −1 (α � = 0) using the eigenvalues of the Laplacian matrix of an associated weighted graph. 1
Application of Chemical Graph Theory for Automated Mechanism Generation
 J. Chem. Inf. Comput. Sci. 2003
"... We present an application of the chemical graph theory approach for generating elementary reactions of complex systems. Molecular species are naturally represented by graphs, which are identified by their vertices and edges where vertices are atom types and edges are bonds. The mechanism is generate ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
We present an application of the chemical graph theory approach for generating elementary reactions of complex systems. Molecular species are naturally represented by graphs, which are identified by their vertices and edges where vertices are atom types and edges are bonds. The mechanism is generated using a set of reaction patterns (subgraphs). These subgraphs are the internal representations for a given class of reaction thus allowing for the possibility of eliminating unimportant product species a priori. Furthermore, each molecule is canonically represented by a set of topological indices (Connectivity Index, Balaban Index, Schulz TI Index, WID Index, etc.) and thus eliminates the probability for regenerating the same species twice. Theoretical background and test cases on combustion of hydrocarbons are presented. 1.
Water Activated Carbon Organics Adsorption Structure Property Relationships
"... Investigation (determination) of chemical compounds properties need time and many resources when is performed by classical way, or experimentations. Nowadays a number of quantitative structureproperty relationships (QSPRs) were developed in order to shorting the research and analysis time of chemic ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
Investigation (determination) of chemical compounds properties need time and many resources when is performed by classical way, or experimentations. Nowadays a number of quantitative structureproperty relationships (QSPRs) were developed in order to shorting the research and analysis time of chemical properties on classes of compounds. The ability of the molecular descriptor family (MDF) was used to produce QSPRs for estimating the adsorption onto activated carbon in water. A number of sixteen organics and theirs adsorption onto activated carbon in water serves for QSPRs obtaining. The MDF methodology include the threedimensional model of the molecules building using the HyperChem software, MDF members generating using a set of Pre Hypertext Processor (PHP) programs, storing using a MySQL database server, and finally with a set of Delphi Multiple Linear Regression programs structureproperty relationships findings. A number of 105319 MDF members enter into multiple linear regressions findings. Five from our best QSPRs are presented, one monovaried, two bivaried and two trivaried models. The MDF QSPR methodology has big potential in finding QSPR models and is proved for adsorption onto activated carbon in water of studied organics. Keywords:
Trees of extremal connectivity index
 Discrete Appl. Math
"... The connectivity index wα(G) of a graph G is the sum of the weights (d(u)d(v)) α of all edges uv of G, where α is a real number (α � = 0), and d(u) denotes the degree of the vertex u. Let T be a tree with n vertices and k pendant vertices. In this paper, we give sharp lower and upper bounds for w1(T ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
The connectivity index wα(G) of a graph G is the sum of the weights (d(u)d(v)) α of all edges uv of G, where α is a real number (α � = 0), and d(u) denotes the degree of the vertex u. Let T be a tree with n vertices and k pendant vertices. In this paper, we give sharp lower and upper bounds for w1(T). Also, for −1 ≤ α < 0, we give a sharp lower bound and a upper bound for wα(T).
On the Randić Index ∗
"... The Randić index of an organic molecule whose molecular graph is G is defined 1 − as the sum of (d(u)d(v)) 2 over all pairs of adjacent vertices of G, where d(u) is the degree of the vertex u in G. In [2], Delorme et al gave a bestpossible lower bound on the Randić index of a trianglefree graph G ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
The Randić index of an organic molecule whose molecular graph is G is defined 1 − as the sum of (d(u)d(v)) 2 over all pairs of adjacent vertices of G, where d(u) is the degree of the vertex u in G. In [2], Delorme et al gave a bestpossible lower bound on the Randić index of a trianglefree graph G with given minimum degree δ(G). In the paper, we first point out a careless mistake in the proof of their result (Theorem 2 of [2]), and then we will show that the result holds when δ(G) ≥ 2.
Partitioning and Lipophilicity in Quantitative StructureActivity Relationships
"... The history of the relationship of biological activity to partition coefficient and related properties is briefly reviewed. The dominance of partition coefficient in quantitation of structureactivity relationships is emphasized, although the importance of other factors is also demonstrated. Various ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
The history of the relationship of biological activity to partition coefficient and related properties is briefly reviewed. The dominance of partition coefficient in quantitation of structureactivity relationships is emphasized, although the importance of other factors is also demonstrated. Various mathematical models of in vivo transport and binding are discussed; most of these involve partitioning as the primary mechanism of transport. The models describe observed quantitative structureactivity relationships (QSARs) well on the whole, confirming that partitioning is of key importance in in vivo behavior of a xenobiotic. The partition coefficient is shown to correlate with numerous other parameters representing bulk, such as molecular weight, volume and surface area, parachor and calculated indices such as molecular connectivity; this is especially so for apolar molecules, because for polar molecules lipophilicity factors into both bulk and polar or hydrogen bonding components. The relationship of partition coefficient to chromatographic parameters is discussed, and it is shown that such parameters, which are often readily obtainable experimentally, can successfully supplant partition coefficient in QSARs. The relationship of aqueous solubility with partition coefficient is examined in detail. Correlations are observed, even with solid compounds, and these can be used to predict solubility. The additive/constitutive nature of partition coefficient is discussed extensively, as are the available schemes for the calculation of