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.

Novel ways of coding the structure of chemical compounds are presented and their use for correlating biological activity is explored. These structure codes take account of the three-dimensional arrangement of the atoms in a molecule, or consider molecular surface properties. These molecular representations have been studied with large datasets; various applications to biological activity studies and the definition of chemical diversity will be presented.