A new 3D molecular structure representation using quantum topology with application to structureproperty relationships
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 @Article{Alsberg:2000:CILS,

author = "Bjorn K. Alsberg and Nathalie MarchandGeneste and
Ross D. King",

title = "A new {3D} molecular structure representation using
quantum topology with application to structureproperty
relationships",

journal = "Chemometrics and Intelligent Laboratory Systems",

year = "2000",

volume = "54",

pages = "7591",

number = "2",

month = "29 " # dec,

keywords = "genetic algorithms, genetic programming, Structure
representation using quantum topology, StruQT,
Quantitative structureactivity relationships, QSAR,
Quantitative structureproperty relationships, QSPR,
Atoms in molecules, AIM, Quantum chemistry, Bader
theory, Multivariate analysis, Partial least squares
regression, 3D structure representation, Variable
selection",

ISSN = "01697439",

owner = "wlangdon",

broken = "http://www.sciencedirect.com/science/article/B6TFP426XTF71/2/36265a259de8f80d4918ee6612612218",

DOI = "doi:10.1016/S01697439(00)001015",

abstract = "We present a new 3D molecular structure representation
based on Richard F.W. Bader's quantum topological atoms
in molecules (AIM) theory for use in quantitative
structureproperty/activity relationship (QSPR/QSAR)
modelling. Central to this structure representation
using quantum topology (StruQT) are critical points
located on the electron density distribution of the
molecules. Other gradient fields such as the Laplacian
of the electron density distribution can also be used.
The type of critical point of particular interest is
the bond critical point (BCP) which is here
characterised by using the following three parameters:
electron density [rho], the Laplacian [nabla]2[rho] and
the ellipticity [epsi]. This representation has the
advantage that there is no need to probe a large number
of lattice points in 3D space to capture the important
parts of the 3D electronic structure as is necessary
in, e.g. comparative field analysis (CoMFA).
We tested the new structure representation by
predicting the wavelength of the lowest UV transition
for a system of 18 anthocyanidins. Different
quantitative structureproperty relationship (QSPR)
models are constructed using several
chemometric/machine learning methods such as standard
partial least squares regression (PLS), truncated PLS
variable selection, genetic algorithmbased variable
selection and genetic programming (GP). These models
identified bonds that either take part in decreasing or
increasing the dominant excitation wavelength. The
models also correctly emphasised on the involvement of
the conjugated [pi] system for predicting the
wavelength through flagging the BCP ellipticity
parameters as important for this particular data set.",
 }
Genetic Programming entries for
Bjorn K Alsberg
Nathalie MarchandGeneste
Ross D King
Citations