Decision support systems for chemical structure representation, reaction modeling, and spectra simulation.
The choice of an appropriate structure coding scheme is the secret to success in QSAR studies. Depending on the problem at hand, 2D or 3D descriptors have to be chosen; the consideration of electronic effects might be crucial, conformational flexibility has to be of special concern. Artificial neural networks, both with unsupervised and with supervised learning schemes, are powerful tools for establishing relationships between structure and physical, chemical, or biological properties. The EROS system for the simulation of chemical reactions is briefly presented and its application to the degradation of s-triazine herbicides is shown. It is further shown how the simulation of chemical reactions can be combined with the simulation of infrared spectra for the efficient identification of the structure of degradation products.
Gasteiger
J
J
Computer-Chemie-Centrum and Institute of Organic Chemistry, University of Erlangen-Nuremberg, Erlangen, Germany.
Bauerschmidt
S
S
Burkard
U
U
Hemmer
M C
MC
Herwig
A
A
Von Homeyer
A
A
Höllering
R
R
Kleinöder
T
T
Kostka
T
T
Schwab
C
C
Selzer
P
P
Steinhauer
L
L
D003661:Decision Support Techniques; D005544:Forecasting; D006540:Herbicides / Q000009:adverse effects / Q000494:pharmacology; D007259:Infrared Rays; D008956:Models, Chemical; D008968:Molecular Conformation; D013329:Structure-Activity Relationship; D014227:Triazines
D006540:Herbicides; D014227:Triazines