- author = "Bjorn K. Alsberg and Nathalie Marchand-Geneste and Ross D. King",
- title = "A new {3D} molecular structure representation using quantum topology with application to structure-property relationships",
- journal = "Chemometrics and Intelligent Laboratory Systems",
- year = "2000",
- volume = "54",
- pages = "75--91",
- number = "2",
- month = "29 " # dec,
- keywords = "genetic algorithms, genetic programming, Structure representation using quantum topology, StruQT, Quantitative structure-activity relationships, QSAR, Quantitative structure-property relationships, QSPR, Atoms in molecules, AIM, Quantum chemistry, Bader theory, Multivariate analysis, Partial least squares regression, 3D structure representation, Variable selection",
- ISSN = "0169-7439",
- owner = "wlangdon",
- broken = "http://www.sciencedirect.com/science/article/B6TFP-426XTF7-1/2/36265a259de8f80d4918ee6612612218",
-
DOI = "
doi:10.1016/S0169-7439(00)00101-5",
-
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
structure-property/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 structure-property relationship (QSPR) models are constructed using several chemometric/machine learning methods such as standard partial least squares regression (PLS), truncated PLS variable selection, genetic algorithm-based 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.",
