ABSTRACT
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.
Subject(s)
Decision Support Techniques , Models, Chemical , Forecasting , Herbicides/adverse effects , Herbicides/pharmacology , Infrared Rays , Molecular Conformation , Structure-Activity Relationship , TriazinesABSTRACT
Organic reactions can be run under a variety of conditions, from laboratory experiments, through technical processes, to combinatorial chemistry. The scope is further extended when the metabolism of compounds and the reactions in the mass spectrometer are included. We present here several concepts: reactors, phases, and modes, which, together with a kinetic modeling, allow the treatment of such a broad scope of organic reactions. These concepts have been implemented in a knowledge-based system, EROS. Several applications of this system to the wide world of organic reactions are given.