The Tools of Computational Chemistry

In studying these questions computational chemists have a selection of methods at their disposal. The main tools available belong to five broad classes  

The Schrodinger equation cannot be solved exactly for any molecule with more than one ( ) electron. Thus approximations are used the less serious these are, the higher the level of the ab initio calculation is said to be. Regardless of its level, an ab initio calculation is based only on basic physical theory (quantum mechanics) and is in this sense from first principles . 

Ab initio calculations are relatively slow the geometry and IR spectra (= the vibrational frequencies) of propane can be calculated at a reasonably high level in minutes on a personal computer, but a fairly large molecule, like a steroid, could take perhaps days. The latest personal computers, with 2 or more GB of RAM and a thousand or more gigabytes of disk space, are serious computational tools and now compete with UNIX workstations even for the demanding tasks associated with high-level ab initio calculations. Indeed, one now hears little talk of workstations , machines costing ca. 15,000 or more [1], 

Molecular mechanics is fast a fairly large molecule like a steroid (e.g. cholesterol, C27H46O) can be optimized in seconds on a powerful desktop computer (a workstation) on a personal computer the job might also take only a few seconds. 

SE calculations will give good answers for molecules for which the program has not been parameterized (otherwise why notjust look up the experimental results ) and this is often the case (MM, too, is parameterized). 

Semiempirical calculations are slower than MM but much faster than ab initio calculations. SE calculations take roughly 100 times as long as MM calculations, and ab initio calculations take roughly 100-1000 times as long as SE. A SE geometry optimization on a steroid might take minutes on a Pentium-type machine. 

Density functional calculations (often called density functional theory (DPT) calculations) are, like ab initio and SE calculations, based on the Schrodinger equation. However, unlike the other two methods, DFT does not calculate a wavefunction, but rather derives the electron distribution (electron density function) directly. A functional is a mathematical entity related to a function. 

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This chapter describes an area of research that is finding new uses every day. The tools of computational chemistry—computers and software—have become so ubiquitous that there are few chemists left today who have not heard of them or used them. However, the reader may reasonably ask Why is there a chapter on computational chemistry in a book on stress testing of pharmaceutically interesting compounds The reason does not lie in a vast number of papers crying for review. Indeed, relatively little work has been published in this area thus far. So what is the reason The objective of this chapter is to increase awareness of how computational chemistry can be used by pharmaceutical chemists to help confront some of the research problems they face in stress testing research. Hence, the nature of this chapter is intended to be primarily tutorial... and perhaps with a little proselytizing for increased use of the powerful techniques of computational chemistry now available. 

Abstract You can calculate molecular geometries, rates and equilibria, spectra, and other physical properties. The tools of computational chemistry are molecular mechanics, ab initio, semiempirical and density functional methods, and molecular dynamics. Computational chemistry is widely used in the pharmaceutical industry to explore the interactions of potential drugs with biomolecules, for example by docking a candidate drug into the active site of an enzyme. It is also used to investigate the properties of solids (e.g. plastics) in materials science. It does not replace experiment, which remains the final arbiter of truth about Nature. 

Chapter 9 is an elaboration of the lecture notes presented by one of us (DBB) at the Workshop on Molecular Modeling at the American Chemical Society National Meeting, Dallas, Texas, April 1989. Rather than being a comprehensive review, the chapter treats the subject matter from a very elementary point of view so as to acquaint newcomers with some of the tools of computational chemistry. At the workshop there was considerable interest expressed in the notes, so hopefully this distribution will help satisfy that need. 

In any case, the tools of computational chemistry are now well established. Essentially all the research-based pharmaceutical and biotechnology companies employ large numbers of computational chemists and are adding more. Is it now safe to use CALD Can we be completely honest about what the technology can and cannot do Or, is it best to stick with CADD Is CAMD better than either CALD or CADD because it is both general and forthright ... 

A chemist must realize that theories, models, and approximations are powerful tools for understanding and achieving research goals. The price of having such powerful tools is that not all of them are perfect. This may not be an ideal situation, but it is the best that the scientihc community has to offer. Chemists are advised to develop an understanding of the nature of computational chemistry approximations and what results can be trusted with any given degree of accuracy. 

The overall scope of this book is the implementation and application of available theoretical and computational methods toward understanding the structure, dynamics, and function of biological molecules, namely proteins, nucleic acids, carbohydrates, and membranes. The large number of computational tools already available in computational chemistry preclude covering all topics, as Schleyer et al. are doing in The Encyclopedia of Computational Chemistry [23]. Instead, we have attempted to create a book that covers currently available theoretical methods applicable to biomolecular research along with the appropriate computational applications. We have designed it to focus on the area of biomolecular computations with emphasis on the special requirements associated with the treatment of macromolecules. 

A general trend which could be noticed over the last few years and which may be expected to develop further in the near future involves a closer coupling between the use of general tools of computational chemistry (ab initio and semi-empirical quantum chemistry, statistical-mechanical simulations) and relaxation theory. When applied to model systems, the computational chemistry methods have the potential of providing new insights on how to develop theoretical models, as well as of yielding estimates of the parameters occurring in the models. 

A clear definition of terms is critical to the snccess of all communication. Particularly in the area of computational chemistry, there is a need to be careful in the nomenclature used to describe predictive tools, since this often helps clarify what approximations have been made in the course of a modeling experiment . For the purposes of this textbook, we will adopt a specific convention for what distinguishes theory, computation, and modeling. 

Why has the practice of computational chemistry skyrocketed in the last few years Try taking this short quiz Chemical waste disposal and computational technology - which of these two keeps getting more and more expensive and which less and less From an economic perspective, at least, theory is enonnously attractive as a tool to reduce the costs of doing experiments. 

Nuclear magnetic resonance spectroscopy is a powerful and widely-used tool for investigating structure. Its utility is enhanced by the use of computational chemistry to aid in the interpretation. In this work we present an example of the use of calculated nuclear magnetic resonance parameters to help elucidate the role of alkoxyalkylsilanes in Ziegler-Natta catalysis. 

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 this chapter, we discuss the main tools of computational chemistry in the context of studying the stability and degradation of pharmaceutical compounds. In a single chapter, it is impossible to teach everything there is to know about computational chemistry, of course. The reader is encouraged to pursue further learning and to try out some computational chemistry software, if they have not already done so. 

Probably, the most important tool of computational chemistry is computer graphics because this provides the interface between the user and the computer. Molecular structure is the universal language of chemists. Therefore, it was an important advance when a user could enter a molecular structure into the computer by simply drawing lines and pointing and clicking on a building menu. 

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. 

We have seen, above, that computational chemistry can sometimes tell us with good reliability whether a molecule can exist. Another important application is to indicate how one molecule gets to be another how chemical reactions occur. Indeed, the prime architect of one of the most useful computational tools, the AMI method (Chapter 6), questioned whether the mechanism of any organic reaction was really known. [36] before the advent of computational chemistry ... 

For example, compounds from the pyridinylimidazole class of p38 inhibitors have served as leads for c-Raf [123] and AlkS [124]. For further details on the role of computational chemistry in kinase inhibitor structure-based design strategies and the range of computational tools being applied in this area see a recent overview by Woolfrey and Weston [89]. 

The impact of the hexacyclinol story, particularly of Rychnovsky s paper, is hard to minimize. This study dramatically demonstrated the power of computational chemistry to aid in structure determination. The fact that the study was undertaken by a syndietic organic chemist spurred many others to utilize this tool as an adjunct in their efforts to identify structure, including the next two case studies. 

A tool of computational chemistry, simulated annealing, is now almost a routine part of solving the tertiary structure of a protein. Also plotted in Figure 16 is the trend of CJACS mentions of a program for simulated annealing, X-PLOR, which is widely used by crystallographers. It is clear that growth in use of the Protein Data Bank and the use of X-PLOR have been nearly parallel since 1989. 

The next two sections of this chapter treat the technical history of computational chemistry, namely the development of the hardware, concepts, and methods used in the field. We discuss first the chemists earliest uses of the computer as a research tool in the 1950s. The first stored program electronic digital computers became available in the early 1950s in chemistry, as in other branches of science, these devices were initially deployed as calculating engines. Next we deal with the expansion of the field of computational chemistry during... 

We make reference to three books that have promoted the use of the computer as a tool for chemists. s- s However, none of these books focuses on computational chemistry in the way we have defined it here. A number of monographs " have appeared in recent years that address the issue of computational chemistry at a somewhat introductory level. These can be helpful to the instructor who wishes to introduce computational chemistry into the curriculum. Only one of these included a computer diskette as part of the textbook the exercises on the diskette cover only a portion of the topics in the text itself. The textbook by Warshel, Computer Modeling of Chemical Reactions in Enzymes and Solutions, is an excellent graduate-level introduction with emphasis on solvent models and macromolecules. ... 

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