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| Johann Gasteiger, Universitaet Erlangen-Nuernberg |
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Professor Johann Gasteiger is head of the Computer-Chemie-Centrum at the University of Erlangen. He is the author of over 170 journal articles, 70 book contributions, and 9 books in the area of cheminformatics and computational chemistry.
Career
1971/72 Postdoc with Prof. Andrew Streitwieser Jr. at University of California, Berkeley, CA, USA: "ab initio calculations on carbanions"
1972-1993 Technical University of Munich: Scientific Assistant - Academic Director
1979 Habilitation: "Chemical Reactions and Reactivity - Models and Algorithms" Privat-Dozent
1988 apl. Professor
since 1994 C3-professor at the University of Erlangen-Nürnberg: foundation of the "Computer-Chemie-Centrum" (together with Prof. Paul von Ragué Schleyer and Priv. Doz. Dr. Tim Clark)
1986-2004 vice-chairman of the Working Party "Computational Chemistry" of the Federation of European Chemical Societies (FECS)
Awards
1991 Gmelin-Beilstein Medal of the German Chemical Society for Achievements in Computer Chemistry
1997 Herman Skolnik Award of the Division of Chemical Information of the American Chemical Society
2005 Mike Lynch Award of the Chemical Structure Association
Editorial Boards - Member of:
- Journal of Chemical Information and Computer Sciences
- Computers and Chemistry
- MATCH - Communications in Mathematical Chemistry
- Quantitative Structure-Activity Relationships
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The Representation of Chemical Structures and its Application to Property Prediction
Johann Gasteiger, Computer-Chemie-Centrum, Universität Erlangen-Nürnberg, D-91052 Erlangen, Germany
The relationships between the structure of a chemical compound and many of its physical, chemical, or biological properties are too complex to be calculated from first principles. This is particularly true for the biological activity of a compound. In this situation an indirect approach has to be taken.[1] First, chemical descriptors have to be calculated for the molecular structure. Then, a relationship between these structure descriptors and the property to be predicted has to be established by inductive learning methods such as statistical or pattern recognition methods or neural networks. Methods for calculating structure descriptors available on the internet will be presented. Furthermore, program packages for data analysis and data mining that can be accessed on the web will be indicated. Of crucial importance for property modeling is the availability of data. Fortunately, increasingly, data become available on the internet. Some applications in modeling properties such as solubility or toxicity will be presented.
[1] Chemoinformatics - A Textbook, J. Gasteiger, T. Engel (Editors), Wiley-VCH, Weinheim, 2003.
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