Zobrazeno 1 - 10
of 241
pro vyhledávání: '"Peter C. Jurs"'
Publikováno v:
Journal of Chemical Information and Modeling. 46:321-333
Virtual screening (VS) has become a preferred tool to augment high-throughput screening(1) and determine new leads in the drug discovery process. The core of a VS informatics pipeline includes several data mining algorithms that work on huge database
Autor:
Linnan He, Peter C. Jurs
Publikováno v:
Journal of Molecular Graphics and Modelling. 23:503-523
Quantitative structure activity relationships (QSAR) are one of the well-developed areas in computational chemistry. In this field, many successful predictive models have been developed for various property, activity or toxicity predictions. However,
Autor:
Rajarshi Guha, Peter C. Jurs
Publikováno v:
Journal of Chemical Information and Modeling. 45:800-806
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 approach is based on a sensitivity analysis of the descriptors. We tested the method on three p
Development of QSAR Models To Predict and Interpret the Biological Activity of Artemisinin Analogues
Autor:
Peter C. Jurs, Rajarshi Guha
Publikováno v:
Journal of Chemical Information and Computer Sciences. 44:1440-1449
This work presents the development of Quantitative Structure-Activity Relationship (QSAR) models to predict the biological activity of 179 artemisinin analogues. The structures of the molecules are represented by chemical descriptors that encode topo
Publikováno v:
Chemical Research in Toxicology. 16:1567-1580
Classification models were developed to provide accurate prediction of genotoxicity of 277 polycyclic aromatic compounds (PACs) directly from their molecular structures. Numerical descriptors encoding the topological, geometric, electronic, and polar
Autor:
Stephen K. Durham, Gregory W. Kauffman, Peter C. Jurs, Laura L. Custer, Greg M. Pearl, Brian E. Mattioni
Publikováno v:
Journal of Chemical Information and Computer Sciences. 43:949-963
Binary quantitative structure-activity relationship (QSAR) models are developed to classify a data set of 334 aromatic and secondary amine compounds as genotoxic or nongenotoxic based on information calculated solely from chemical structure. Genotoxi
Publikováno v:
Journal of Medicinal Chemistry. 46:1066-1080
A data set of 348 urea-like compounds that inhibit the soluble epoxide hydrolase enzyme in mice and humans is examined. Compounds having IC(50) values ranging from 0.06 to500 microM (murine) and 0.10 to500 microM (human) are categorized as active or
Autor:
Su J. Patankar, Peter C. Jurs
Publikováno v:
Journal of Computer-Aided Molecular Design. 17:155-171
HIV protease inhibitors are being used as frontline therapy in the treatment of HIV patients. Multi-drug-resistant HIV mutant strains are emerging with the initial aggressive multi-drug treatment of HIV patients. This necessitates continued search fo
Autor:
Su J. Patankar, Peter C. Jurs
Publikováno v:
Journal of Chemical Information and Computer Sciences. 42:1053-1068
The design and blood brain barrier crossing of glycine/NMDA receptor antagonists are of significant interest in pharmaceutical research. The use of these antagonists in stroke or seizure reduction ...
Autor:
Brian E. Mattioni, Peter C. Jurs
Publikováno v:
Journal of Chemical Information and Computer Sciences. 42:232-240
Quantitative structure-property relationships (QSPR) are developed to correlate glass transition temperatures and chemical structure. Both monomer and repeat unit structures are used to build several QSPR models for Parts 1 and 2 of this study, respe