Novel 3D-descriptors using Triplets Of Pharmacophoric Points (TOPP) were evaluated in QSAR-studies on 80 apoptosis-inducing 4-aryl-4H-chromenes. A predictive QSAR model was obtained using PLS, confirmed by means of internal and external validations. Performance of the TOPP approach was compared with that of other 2D- and 3D-descriptors; statistical analysis indicates that TOPP descriptors perform best. A ranking of TOPP>GRIND>BCI 4096=ECFP>FCFP>GRID-GOLPE>>DRAGON>>>MDL 166 was achieved. Finally, in a 'consensus' analysis predictions obtained using the single methods were compared with an average approach using six out of eight methods. The use of the average is statistically superior to the single methods. Beyond it, the use of several methods can help to easily investigate the presence/absence of outliers according to the 'consensus' of the predicted values: agreement among all the methods indicates a precise prediction, whereas large differences between predicted values (for the same compounds by different methods) would demand caution when using such predictions.
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http://dx.doi.org/10.1016/j.bmc.2007.06.051 | DOI Listing |
Lancet Reg Health West Pac
November 2024
Social Policy Research Centre, University of New South Wales, Sydney, NSW, Australia.
Background: Culture and its practice is a recognised, but not well understood factor, in Aboriginal health and wellbeing. Our study aimed to explore how health and wellbeing are phenomenologically connected to cultural practices, foods, medicines, languages, and Country, through the platform of 'on-Country' camps facilitated by Aboriginal cultural knowledge holders in NSW, Australia.
Methods: Our study is based on a collaboration between knowledge holders from freshwater and saltwater cultures, and Aboriginal and non-Aboriginal researchers.
F1000Res
January 2018
Agrosphäre (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52428, Germany.
Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology.
View Article and Find Full Text PDFJ Comput Chem
July 2018
Department of Mathematics, Michigan State University, East Lansing, Michigan, 48824.
Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction accuracy depends crucially on molecular descriptors which are typically derived from a theoretical understanding of the chemistry and physics of small molecules.
View Article and Find Full Text PDFCurr Opin Plant Biol
April 2015
Donald Danforth Plant Science Center, St. Louis, MO 63132, USA. Electronic address:
The plant phenotype is infinite. Plants vary morphologically and molecularly over developmental time, in response to the environment, and genetically. Exhaustive phenotyping remains not only out of reach, but is also the limiting factor to interpreting the wealth of genetic information currently available.
View Article and Find Full Text PDFComput Chem Eng
November 2013
Department of Chemical and Petroleum Engineering, University of Kansas, Lawrence, KS, United States.
Lyophilization can induce aggregation in therapeutic proteins, but the relative importance of protein structure, formulation and processing conditions are poorly understood. To evaluate the contribution of protein structure to lyophilization-induced aggregation, fifteen proteins were co-lyophilized with each of five excipients. Extent of aggregation following lyophilization, measured using size-exclusion chromatography, was correlated with computational and biophysical protein structural descriptors via multiple linear regression.
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