Computational Chemist

ELECTRAS - web-based data analysis system. The software supports 2x2 different modes of action the modes for expert and novice engineers and the modes for expert and novice computational chemists. htip //www2.chemie.uni-erlangen.de/projects/eDAS/index.himl... 

UyperC hem ( omputaiional ( hernislry is for both novice computational chemists and for scientists with advanced knowledge and experience. 

Containsnine reviews in computational chemistry by various experts. This book is particularly useful for beginning computational chemists. Six chapters address issues relevant to HyperChem. including semi-empirical quantum mechanics... 

A textbook describing the theory associated with calculation s of Ih e electronic structure of molecti lar system s. While the book focuses on ab ini/rci calculation s, much of the in formation is also relevant to semi-empirical methods. The sections on the Hartree-fock an d Con figuration ItUeracTion s tn elh ods, in particular, apply to HyperChem. fhe self-paced exercisesare useful for the beginning computational chemist. 

The term theoretical chemistry may be defined as the mathematical description of chemistry. The term computational chemistry is generally used when a mathematical method is sufficiently well developed that it can be automated for implementation on a computer. Note that the words exact and perfect do not appear in these definitions. Very few aspects of chemistry can be computed exactly, but almost every aspect of chemistry has been described in a qualitative or approximately quantitative computational scheme. The biggest mistake a computational chemist can make is to assume that any computed number is exact. However, just as not all spectra are perfectly resolved, often a qualitative or approximate computation can give useful insight into chemistry if the researcher understands what it does and does not predict. 

Quantum mechanics gives a mathematical description of the behavior of electrons that has never been found to be wrong. However, the quantum mechanical equations have never been solved exactly for any chemical system other than the hydrogen atom. Thus, the entire held of computational chemistry is built around approximate solutions. Some of these solutions are very crude and others are expected to be more accurate than any experiment that has yet been conducted. There are several implications of this situation. First, computational chemists require a knowledge of each approximation being used and how accurate the results are expected to be. Second, obtaining very accurate results requires extremely powerful computers. Third, if the equations can be solved analytically, much of the work now done on supercomputers could be performed faster and more accurately on a PC. 

As computational chemistry has become easier to use, professional computational chemists have shifted their attention to more difficult modeling problems. No matter how easy computational chemistry becomes, there will always be problems so difficult that only an expert in the field can tackle them. 

Apractical introduction to molecular mechanics and semi-empirical quantum mechanics calculations, with extensive examples from the MMP2 (not in HyperChem), MINDO/3, and MNDO methods. One of the more accessible books for new computational chemists. 

Monte Carlo (MC) techniques for molecular simulations have a long and rich history, and have been used to a great extent in studying the chemical physics of polymers. The majority of molecular modeling studies today do not involve the use of MC methods however, the sampling capabiUty provided by MC methods has gained some popularity among computational chemists as a result of various studies (95—97). Relevant concepts of MC are summarized herein. 

A tmism of computational chemistry is that chemists will always want to model ever larger systems, or smaller systems, at ever more accurate levels of approximation. The total miming time of jobs has, in general, not lowered dramatically. Computational chemists still perform calculations that take several days to complete. However, today the molecules can be much larger and the quaUty of the calculations better. 

Prior to the widespread usage of methods based on Density Functional Theory, the MP2 method was one of the least expensive ways to improve on Hartree-Fock and it was thus often the first correlation method to be applied to new problems. It can successfully model a wide variety of systems, and MP2 geometries are usually quite accurate. Thus, MP2 remains a very useful tool in a computational chemist s toolbox. We ll see several examples of its utility in the exercises. 

The term ab initio is often used in theoretical chemistry and even in the general chemistry literature. In the paper I try to explore precisely what this term means. Does it really refer to calculations carried out from first principles without any recourse whatsoever to empirical data Surprisingly, I found that theoretical and computational chemists use this term with... 

The spread of technology at pharmaceutical companies also meant that secretaries were given word processors (such as the Wang machines) to use in addition to typewriters, which were still needed for filling out forms. Keyboarding was the domain of the secretaries, the data entry technicians, and the computational chemists. Only a few managers and scientists would type their own memos and articles. 

Figure 1.1 Offices used by computational chemists were filled with stacks of printouts and banks of file cabinets with legacy card decks. This photograph was taken in 1982, but the appearance of the environs had not changed much since the mid-1970s.

Computational chemists in the pharmaceutical industry also expanded from their academic upbringing by acquiring an interest in force field methods, QSAR, and statistics. Computational chemists with responsibility to work on pharmaceuticals came to appreciate the fact that it was too limiting to confine one s work to just one approach to a problem. To solve research problems in industry, one had to use the best available technique, and this did not mean going to a larger basis set or a higher level of quantum mechanical theory. It meant using molecular mechanics or QSAR or whatever. 

Unfortunately, the tension between the computational chemists and the medicinal chemists at pharmaceutical companies did not ease in the 1970s. Medicinal chemists were at the top of the pecking order in corporate research laboratories. This was an industry-wide problem revealed in conversations at scientific meetings where computational chemists from industry (there were not many) could informally exchange their experiences and challenges. (Readers should not get the impression that the tension between theoreticians and experimentalists existed solely in the business world. It also existed in academic chemistry departments.)... 

The situation was that as medicinal chemists pursued an SAR, calculations by the computational chemists might suggest a structure worthy of synthesis. Maybe the design had the potential of being more active. But the computational chemist was totally dependent on the medicinal chemist to test the hypothesis. Suddenly, the medicinal chemist saw himself going from being the... 

In an outreach to the medicinal chemists at Lilly, a one-week workshop was created and taught in the research building where the organic chemists were located. (The computational chemists were initially assigned office space with the analytical chemists and later with the biologists.) The workshop covered the basic and practical aspects of performing calculations on... 

The computational chemists were able to form collaborations with their fellow physical chemists. Some of the research questions dealt with molecular conformation and spectroscopy. The 1970s were full of small successes such as finding correlations between calculated and experimental properties. Some of these correlations were published. Even something so grand as the de novo design of a pharmaceutical was attempted but was somewhat beyond reach. 

Data Bank (PDB) [56], Computational chemists recognized that these compilations of 3D molecular structures would prove very useful, especially as more pharmaceutically relevant compounds were deposited. The CSD was supported by subscribers, including pharmaceutical companies. On the other hand, the PDB was supported by American taxpayers. 

Sciences (JCICS) in an attempt to meet the needs of today s computational chemists. JCICS was becoming the most popular venue for computational chemists to publish work on combinatorial library designs (see Fig. 1.2 and Section 1.6 on the 1990s). 

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