Search Results - "Jurs, Peter C"

Determining the Validity of a QSAR Model − A Classification Approach by Guha, Rajarshi, Jurs, Peter C

“…The determination of the validity of a QSAR model when applied to new compounds is an important concern in the field of QSAR and QSPR modeling. Various scoring…”
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QSAR and Classification of Murine and Human Soluble Epoxide Hydrolase Inhibition by Urea-Like Compounds by McElroy, Nathan R, Jurs, Peter C, Morisseau, Christophe, Hammock, Bruce D

“…A data set of 348 urea-like compounds that inhibit the soluble epoxide hydrolase enzyme in mice and humans is examined. Compounds having IC50 values ranging…”
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Classification of Multidrug-Resistance Reversal Agents Using Structure-Based Descriptors and Linear Discriminant Analysis by Bakken, Gregory A, Jurs, Peter C

“…Linear discriminant analysis is used to generate models to classify multidrug-resistance reversal agents based on activity. Models are generated and evaluated…”
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Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies by Stanton, David T, Jurs, Peter C

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Prediction of Glass Transition Temperatures from Monomer and Repeat Unit Structure Using Computational Neural Networks by Mattioni, Brian E, Jurs, Peter C

“…Quantitative structure−property relationships (QSPR) are developed to correlate glass transition temperatures and chemical structure. Both monomer and repeat…”
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Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure by Wessel, Matthew D, Jurs, Peter C, Tolan, John W, Muskal, Steven M

“…The absorption of a drug compound through the human intestinal cell lining is an important property for potential drug candidates. Measuring this property,…”
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Interpreting Computational Neural Network QSAR Models:  A Measure of Descriptor Importance by Guha, Rajarshi, Jurs, Peter C

“…We present a method to measure the relative importance of the descriptors present in a QSAR model developed with a computational neural network (CNN). The…”
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Local Lazy Regression:  Making Use of the Neighborhood to Improve QSAR Predictions by Guha, Rajarshi, Dutta, Debojyoti, Jurs, Peter C, Chen, Ting

“…Traditional quantitative structure−activity relationship (QSAR) models aim to capture global structure−activity trends present in a data set. In many…”
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Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors by Guha, Rajarshi, Jurs, Peter C

“…A QSAR modeling study has been done with a set of 79 piperazyinylquinazoline analogues which exhibit PDGFR inhibition. Linear regression and nonlinear…”
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Assessing the reliability of a QSAR model's predictions by He, Linnan, Jurs, Peter C.

“…Quantitative structure activity relationships (QSAR) are one of the well-developed areas in computational chemistry. In this field, many successful predictive…”
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Prediction of Fathead Minnow Acute Toxicity of Organic Compounds from Molecular Structure by Eldred, Donald V, Weikel, Cara L, Jurs, Peter C, Kaiser, Klaus L. E

“…Interest in the prediction of toxicity without the use of experimental data is growing, and quantitative structure−activity relationship (QSAR) methods are…”
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Prediction of carbon-13 nuclear magnetic resonance chemical shifts by artificial neural networks by Anker, Lawrence S, Jurs, Peter C

“…Empirical models relating atom-based structural descriptors to 13C NMR chemical shifts have been used to accurately simulate 13C NMR spectra for compounds…”
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QSAR/QSPR Studies Using Probabilistic Neural Networks and Generalized Regression Neural Networks by Mosier, Philip D, Jurs, Peter C

“…The Probabilistic Neural Network (PNN) and its close relative, the Generalized Regression Neural Network (GRNN), are presented as simple yet powerful neural…”
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Development of QSAR Models To Predict and Interpret the Biological Activity of Artemisinin Analogues by Guha, Rajarshi, Jurs, Peter C

“…This work presents the development of Quantitative Structure−Activity Relationship (QSAR) models to predict the biological activity of 179 artemisinin…”
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Prediction of Aqueous Solubility of Heteroatom-Containing Organic Compounds from Molecular Structure by McElroy, Nathan R, Jurs, Peter C

“…The use of quantitative structure−property relationships (QSPRs) to predict aqueous solubilities (log S) of heteroatom-containing organic compounds from their…”
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Interpreting Computational Neural Network Quantitative Structure−Activity Relationship Models:  A Detailed Interpretation of the Weights and Biases by Guha, Rajarshi, Stanton, David T, Jurs, Peter C

“…In this work, we present a methodology to interpret the weights and biases of a computational neural network (CNN) quantitative structure−activity relationship…”
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Quantitative structure-retention relationship studies of sulfur vesicants by Woloszyn, Thomas F, Jurs, Peter C

“…A study that used quantitative structure-retention relationship methods to relate the observed Kovats retention indices of sulfur vesicants with their…”
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R-NN Curves:  An Intuitive Approach to Outlier Detection Using a Distance Based Method by Guha, Rajarshi, Dutta, Debojyoti, Jurs, Peter C, Chen, Ting

“…Libraries of chemical structures are used in a variety of cheminformatics tasks such as virtual screening and QSAR modeling and are generally characterized…”
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Generation of QSAR sets with a self-organizing map by Guha, Rajarshi, Serra, Jon R., Jurs, Peter C.

“…A Kohonen self-organizing map (SOM) is used to classify a data set consisting of dihydrofolate reductase inhibitors with the help of an external set of Dragon…”
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Scalable Partitioning and Exploration of Chemical Spaces Using Geometric Hashing by Dutta, Debojyoti, Guha, Rajarshi, Jurs, Peter C, Chen, Ting

“…Virtual screening (VS) has become a preferred tool to augment high-throughput screening and determine new leads in the drug discovery process. The core of a VS…”
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