1,721,082 research outputs found
Simplified Molecular Input-Line Entry System and International Chemical Identifier in the QSAR Analysis of Styrylquinoline Derivatives as HIV-1 Integrase Inhibitors
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QSAR Models for Toxicity of Organic Substances to Daphnia magna Built up by Using the CORAL Freeware
No full textCORAL (CORrelations And Logic, ) is a freeware available on the Internet. This freeware is designed to build up quantitative structure property/activity relationships. The molecular structure for CORAL should be represented by the simplified molecular input line entry system (SMILES). Optimal descriptors calculated with SMILES are a mathematical function of the presence or absence of SMILES elements. The essence of this approach is the calculation of correlation weights for each element or combination of the elements by the Monte Carlo method. These coefficients serve to calculate the descriptors correlated with the endpoint for the training set, hoping that this correlation will also hold for the external test set. These descriptors can be improved by taking into account global physicochemical situations in molecules. An example of the physicochemical situation is the presence of oxygen and nitrogen. One can calculate these situations with SMILES and represent them by combining 0 (absence) and 1 (presence). The involving in the modelling of correlation weights of aforementioned physicochemical situations gave improvement in accuracy of models of toxicity to Daphnia magna for test set: ntest = 75, r2 = 0.7322, r2pred = 0.7193, r2m = 0.6549 (without correlation weights of the physicochemical situations); and ntest = 75, r2 = 0.7897, r2pred = 0.7790, r2m = 0.6850 (with aforementioned correlation weights of physicochemical situations)
Rigid and flexible topological indices: variety of the representations of the molecular structures.
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Co-evolutions of correlations for QSAR of toxicity of organometallic and inorganic substances: An unexpected good prediction based on a model that seems untrustworthy
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CORAL: Quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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