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作者： Heather L. Ciallella Elena Chung Daniel P. Russo Hao Zhu

Advances in high-throughput screening (HTS) revolutionized the environmental and health sciences data landscape. However, new compounds still need to be experimentally synthesized and tested to obtain HTS data, which will still be costly and time-consuming when a large set of new compounds need to be studied against many tests. quantitative structure–activity relationship (QSAR) modeling is a standard method to fill data gaps for new compounds. The major challenge for many toxicologists, especially those with limited computational backgrounds, is efficiently developing optimized QSAR models for each assay with missing data for certain test compounds. This chapter aims to introduce a freely available and user-friendly QSAR modeling workflow, which trains and optimizes models using five algorithms without the need for a programming background. less

关键词： High-throughput Screening Python Decision Tree Statistical Calculation Models quantitative structure–activity relationships Predictions

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Physicochemical properties of exogenous molecules correlated with their biological efficacy as protectors against carcinogenesis and inflammation (vol 32, pg 393, 2013)

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INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY 2013年第3期32卷 I-I页

作者： Bensasson, Rene V. Sowlati-Hashjin, Shahin Zoete, Vincent Dauzonne, Daniel Matta, Cherif F. [a] Molécules de Communication et Adaptation des Microorganismes UMR 7245 CNRS/MNHN Muséum National d’Histoire Naturelle F-75005 Paris France [b] Department of Chemistry Mount Saint Vincent University Halifax Nova Scotia Canada B3M 2J6 [c] Department of Chemistry Saint Mary’s University Halifax Nova Scotia Canada B3H 3C3 [d] Molecular Modeling Group Swiss Institute of Bioinformatics CH-1015 Lausanne Switzerland [e] Institut Curie Section de Recherche 26 rue d’Ulm F-75005 Paris France [f] CNRS UMR 176 26 rue d’Ulm F-75005 Paris France

A large number of molecules referred to here as M, several of which are extracted from plants, belong to different chemical classes and exhibit chemoprotective activity against carcinogenesis and inflammation in rodents and in vitro cell culture. The aim of this article is to review and analyse our previous investigations on linear correlations observed between physicochemical parameters of the tested molecules M and their biological efficacies measured in vivo and/or in vitro. The principal physicochemical parameters studied characterise the energy required to oxidise M into M?+, E(M?+/M), namely its first vertical ionisation potential, and the energy liberated by the addition of an electron to M, i.e. its vertical electron affinity. These physicochemical properties were determined experimentally using diverse techniques and theoretically using mainly semiempirical (AM1) and density functional theory (DFT) quantum mechanical methods. View full text

关键词： cancer chemoprevention antiinflammation ionisation potential electron affinity quantum chemical calculations quantitative structure–activity relationships diphenols phenylpropenoids flavonoids triterpenoids phenols benzoic and salicylic acids

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Malarial Kinases: Novel Targets for In Silico Approaches to Drug Discovery

Malarial Kinases: Novel Targets for In Silico Approaches to ...

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作者： Kristen M. Bullard Robert Kirk DeLisle Susan M. Keenan

Malaria, the disease caused by infection with protozoan parasites from the genus Plasmodium, claims the lives of nearly 1 million people annually. Developing nations, particularly in the African Region, bear the brunt of this malaria burden. Alarmingly, the most dangerous etiologic agent of malaria, Plasmodium falciparum, is becoming increasingly resistant to current first-line antimalarials. In light of the widespread devastation caused by malaria, the emergence of drug-resistant P. falciparum strains, and the projected decrease in funding for malaria eradication that may occur over the next decade, the identification of promising new targets for antimalarial drug design is imperative. P. falciparum kinases have been proposed as ideal drug targets for antimalarial drug design because they mediate critical cellular processes within the parasite and are, in many cases, structurally and mechanistically divergent when compared with kinases from humans. Identifying a molecule capable of inhibiting the activity of a target enzyme is generally an arduous and expensive process that can be greatly aided by utilizing in silico drug design techniques. Such methods have been extensively applied to human kinases, but as yet have not been fully exploited for the exploration and characterization of antimalarial kinase targets. This review focuses on in silico methods that have been used for the evaluation of potential antimalarials and the Plasmodium kinases that could be explored using these techniques. less

关键词： Plasmodium falciparum Drug Docking Pharmacophore Kinase Antimalarials quantitative structure–activity relationships

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Developmental Toxicity Prediction

Developmental Toxicity Prediction

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作者： Raghuraman Venkatapathy Nina Ching Wang

Developmental toxicity may be estimated using commercial and noncommercial software that is already available in the market and/or literature, or models may be built from scratch using both commercial and noncommercial software packages. In this chapter, commonly available software programs that can predict the developmental toxicity of chemicals are described. In addition, a method for developing qualitative structure–activity relationship (SAR) models to predict the developmental toxicity of chemicals qualitatively (yes/no prediction) and quantitative structure–activity relationship (QSAR) models to predict quantitative estimates (e.g., LOAEL) of developmental toxicants is also described in this chapter. Additional information described in this chapter include methods to predict physicochemical properties of chemicals that can be used as descriptor variables in the model building process, statistical methods that be used to build QSAR models as well as methods to validate the models that are developed. Most of the methods described in this chapter can be used to develop models for health endpoints other than developmental toxicity as well. less

关键词： Statistical Calculation Molecular Modeling Drug Labeling Machine Learning Expert Systems Regression Analysis Principal Component Analysis Neural Network Model Modeling And Simulation Crystallization PCR Molecular Engineering Data Mining MATLAB Oryctolagus cuniculus Daphnia magna Tetrahymena pyriformis Homo sapiens Rattus norvegicus Mus musculus Modeling Model validation Statistical methods QSAR Developmental toxicity structure–activity relationships Model development Toxicity prediction Applicability domain quantitative structure–activity relationships

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QSAR and 3D-QSAR analysis of structurally diverse ALS inhibitors: sulfonylureas and triazolopyrimidine-2-sulfonamides

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Pest Management Science 1999年第12期55卷

作者： Guangfu Yang Huayin Liu Huazheng Yang Institute of Organic Synthesis Central China Normal University Wuhan 430079 P R China Institute of Elemento-Organic Chemistry Nankai University Tianjin 300071 P R China

For the purpose of better understanding the molecular mechanism of action of sulfonylurea and sulfonamide herbicides, the quantitative relationship between their structure and herbicidal activity against rape, Brassica campestris L, was analysed using physicochemical parameters and regression analysis and comparative molecular field analysis (CoMFA). The results showed that the structure–activity relationships of the two sets of compounds were identical, which suggested that the two different sets of compounds affect a common region of the receptor site. The CoMFA results were consistent with those derived from traditional QSAR analysis. Combining the traditional QSAR analysis with the CoMFA results, we can conclude that the variations in the herbicidal activity of the two sets of ALS inhibitors were governed dominantly by the three-dimensional steric and electrostatic field parameters of molecules participating in the interaction with the receptor site and there is apparently an optimum electronic property (Σσ or pKa) for the molecules to fit the receptor. © 1999 Society of Chemical Industry

关键词： acetolactate synthase inhibitors quantitative structure–activity relationships CoMFA sulfonylurea triazolopyrimidine-2-sulfonamide herbicides

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Synthesis and structure–activity relationships for the Fungicidal activity ofO,O-bisarylsec-butylphosphonates

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Pest Management Science 1999年第2期50卷

作者： Doyeli Sanyal Nripendra K. Roy Division of Agricultural Chemicals Indian Agricultural Research Institute New Delhi India

Sixteen O , O -bisaryl sec -butylphosphonates have been synthesised by condensing sec -butylphosphonyl dichloride with substituted phenols. The compounds were tested against two phytopathogenic fungi, Rhizoctonia bataticola and Helminthosporium oryzae . The most active compound against R . bataticola is O , O -bis(3-methylphenyl) sec -butylphosphonate (ED 50 29·56 mg litre -1 ) and against H . oryzae , O , O -bis(2-chlorophenyl) sec -butylphosphonate and its para -chloro analogue (ED 50 0·14 and 0·13 mg litre -1 respectively). quantitative structure–activity relationships on the fungicidal activity have been analysed by means of multiple regression analysis using physicochemical substituent parameters. Electronic parameters viz., σ and F have expressed significant variability in fungitoxicity against both the fungi, viz. R . bataticola and H . oryzae . Hydrophobic and steric parameters are also found to be important in the correlation studies. © 1997 SCI.

关键词： synthesis fungicidal activity quantitative structure–activity relationships Rhizoctonia bataticola Helminthosporium oryzae O,O-bisaryl sec-butylphosphonates

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UK USAGE OF CHEMOMETRICS AND ARTIFICIAL-INTELLIGENCE IN QSAR ANALYSIS

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JOURNAL OF CHEMOMETRICS 1990年第5期4卷 355-360页

作者： ROSE, VS HYDE, RM MACFIE, HJH Department of Physical Sciences Wellcome Research Laboratories South Eden Park Road Beckenham Kent BR3 3BS U.K. AFRC Institute of Food Research Reading Laboratory Shinfield Reading RG2 9AT U.K.

A survey of members of the U.K. QSAR Discussion Group has been made to determine the extent of use and development of chemometric and artificial intelligence (AI) methods in the analysis of multivariate quantitative structure-activity relationship (QSAR) data in the U.K. Chemometric methods were found to be well established in both industrial and educational establishments and there are significant method development occurring. AI methods were not employed to any greater extent and the general level of interest in these techniques was low compared to chemometric methods. A requirement for more education in multivariate statistical methods and regression methods was indicated. A need for a user-friendly, comprehensive, commercially available multivarate statistical package containing multivariate stability testing and regression of diagnostic methods was identified.

关键词： Pattern recognition U.K. chemometrics usage quantitative structure–activity relationships Artificial intelligence

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