Computational chemistry, relationship

N.B. Chapman, J. Shorter (Eds.), Advances in Linear Free Energy Relationships, Plenum Press, London, 1972. po] N.B. Chapman, J. Shorter (Eds.), Correlation Analysis in Chemistry, Plenum Press, London, 1978. pi] J. Shorter, Linear Free Energy Relationships (LEER), in Encyclopedia of Computational Chemistry, Vol. 2, P.v.R. Schleyer, N.L. Ailinger, T. Clark,... 

P. C. Juts, Quantitative structure-property relationships, in Encyclopedia of Computational Chemistry, Volume 4, P. v. R. Schleyer, N. L. Allinger, T. Qaik, J. Gasteiger, P. A. KoUman, H. F. Schaefer III and P.R. Schreiner (Eds.), John Wiley Sons, Chichester, 1998, pp. 2320-2330. 

K and G M Crippen 1986. Atomic Physicochemical Parameters for Three-dimensional Struc-directed Quantitative Structure-Activity Relationships. I. Partition Coefficients as a Measure ydrophobicity. Journal of Computational Chemistry 7 565-577. 

E Novellino and Y C Martin 1997 Approaches to Three-dimensional Quantitative Structure-vity Relationships. In Lipkowitz K B and D B Boyd (Editors) Reviews in Computational Chemistry ime 11. New York, VCH Publishers, pp, 183-240,... 

In order to fully exploit the information contained in the X-ray structures, interdisciplinary research is required, involving such disciplines as computational chemistry, medicinal chemistry, and molecular pharmacology in partnership with X-ray crystallography. In this context, the X-ray structures provide an excellent springboard for studying structure-activity/selectivity relationships, as well as the structure-based design of new ligands. 

Kurt Varmuza was bom in 1942 in Vienna, Austria. He studied chemistry at the Vienna University of Technology, Austria, where he wrote his doctoral thesis on mass spectrometry and his habilitation, which was devoted to the field of chemometrics. His research activities include applications of chemometric methods for spectra-structure relationships in mass spectrometry and infrared spectroscopy, for structure-property relationships, and in computer chemistry, archaeometry (especially with the Tyrolean Iceman), chemical engineering, botany, and cosmo chemistry (mission to a comet). Since 1992, he has been working as a professor at the Vienna University of Technology, currently at the Institute of Chemical Engineering. 

Structure-activity relationships X-ray crystallography, nuclear magnetic resonance, computational chemistry... 

P. Piecuch and K. Kowalski, In search of the relationship between multiple solutions characterizing coupled-cluster theories, in Computational Chemistry Reviews of Current Trends, Vol. 5 (J. Leszczynski, ed.), World Scientific, New York, 2000, p. 1. 

Shorter, J. (1998) Linear free energy relationships (LFER). In Encyclopedia of computational chemistry. P. von Rague Schleyer (ed.). John Wiley Sons, Chichester, Vol. 2, pp. 1487-1496. 

Ghose, A.K., Pritchett, A. and Crippen, G.M. (1988) Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships. 3. Modeling hydrophobic interactions. Journal of Computational Chemistry, 9, 180. 

Computational chemistry methodology is finding increasing application to the design of new flavoring agents. This chapter surveys several useful techniques linear free energy relationships, quantitative structure-activity relationships, conformational analysis, electronic structure calculations, and statistical methods. Applications to the study of artificial sweeteners are described. 

The finding of an exact solution for the scattering of an electron from a hydrogen atom exemplifies the current power of computational chemistry. In the mid-1990 s, specialized workstations were necessary to carry out calculations, and an ab initio calculation on even a small molecule could take an entire afternoon. Now, a desktop computer can run complex calculations in minutes. All students have at their fingertips the means to explore structure-function relationships, or to construct sophisticated models of chemical systems. In the coming decades, computational chemistry will become an integral part of most chemists work, so our students must learn how to use computational methods and how to determine which ones are appropriate for their applications. 

All of these points being made, even computational chemistry is not without cost. In general, the more sophisticated the computational model, the more expensive in terms of computational resources. The talent of the well-trained computational chemist is knowing how to maximize the accuracy of a prediction while minimizing the investment of such resources. A primary goal of this text is to render more clear the relationship between accuracy and cost for various levels of theory so that even relatively inexperienced users can make informed assessments of the likely utility (before the fact) or credibility (after the fact) of a given calculation. 

Statistical mechanics is, obviously, a course unto itself in the standard chemistry/physics curriculum, and no attempt will be made here to introduce concepts in a formal and rigorous fashion. Instead, some prior exposure to the field is assumed, or at least to its thermodynamical consequences, and the fundamental equations describing the relationships between key thermodynamic variables are presented without derivation. From a computational-chemistry standpoint, many simplifying assumptions make most of the details fairly easy to follow, so readers who have had minimal experience in this area should not be adversely affected. 

The scope of computational chemistry can be inferred from the methodologies it encompasses. Some of the more common tools include computer graphics, molecular modeling, quantum chemistry, molecular mechanics (MM), statistical analysis of structure-property relationships, and data management (informatics). As with any dynamic field of research, computational... 

In 1982 Ayerst Laboratories in Montreal became the first company in Canada to install a commercial software tool (the SYBYL suite from Tripos Associates) to help in the development of pharmacophoric models from structure-activity relationships. The installation of the software was the second ever, worldwide, by a company and is a testimonial to the foresight of the director of medicinal chemistry, Dr. Leslie Humber, for having championed its installation. Dr. Adi M. Treasurywala, then an organic chemist with some experience in medicinal chemistry, became the first industrial computational chemist in Canada that year. The use of modeling approaches contributed in a minor but significant way to the discovery of the compound known as Tolrestat, which was an inhibitor of lens aldose reductase. This led to the acknowledgment of Treasurywala as a coinventor of the drug on several patents that were filed in this research area. Approximately in 1983, Ayerst closed down its discovery effort in Canada and moved to Princeton, New Jersey, where an expanded effort in the area of computational chemistry continues. 

Potential energy surfaces are important because they aid us in visualizing and understanding the relationship between potential energy and molecular geometry, and in understanding how computational chemistry programs locate and characterize structures... 

The potential energy surface (PES) is a central concept in computational chemistry. A PES is the relationship - mathematical or graphical - between the energy of a molecule (or a collection of molecules) and its geometry. 

Famini, G.R. and Wilson, L.Y., Linear free energy relationships using quantum mechanical descriptors, in Reviews in Computational Chemistry, Vol. 18, Lipkowitz, K.B. and Boyd, D.B., Eds., Wiley-VCH, New York, 2002, pp. 211-255. 

Scarfe, G.B., Wilson, I.D., Wame, M.A., Holmes, E., Nicholson, J.K., and Lindon, J.C., Structure-metabolism relationships of substituted anilines prediction of N-acetylation and N-oxanilic acid formation using computational chemistry, Xenobiotica, 32, 267-277, 2002. 

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