refined Computational_chemistry Information, explanation, recent texts, monographs, and related patents.
Information & explanations, latest texts & monographs on Computational_chemistry (including recent related patents.)


Computational chemistry

Computational chemistry is the branch of theoretical chemistry whose major goals are to create efficient computer programs that calculate the properties of molecules (such as total energy, dipole moment, vibrational frequencies) and to apply these programs to concrete chemical objects. It is also sometimes used to cover the areas of overlap between computer science and chemistry. There are several major areas within this topic
  • The computational representation of atoms and molecules
  • Approaches to storing and searching data on chemical entities ( Chemical database )
  • approaches to identifying patterns and relationships between chemical structures and their properties ( QSPR )
  • the theoretical elucidation of structure based on simulation of forces
  • computational approaches to help in the efficient synthesis of compounds
  • computational approaches to design molecules that interact in specific ways with other molecules, especially in drug design
The programs used in computational chemistry are based on many different quantum-chemical methods that solve the molecular Schrödinger equation. The methods that do not include empirical or semi-empirical parameters in their equations are called ab initio methods and are currently of the greatest use in computational chemistry. The most popular classes of ab initio methods are: Hartree-Fock, Moller-Plesset perturbation theory, configuration interaction, coupled cluster, reduced density matrices and density functional theory. Each class contains several methods that use different variants of the of the corresponding class, typically geared either to calculating a specific molecular property, or, to application to a special set of molecules. The abundance of these approaches shows that there is no single method suitable for all purposes. It is, in principle, possible to use one exact method (for example, full configuration interaction) and apply it to all the molecules, but, although such methods are well-known and available in many programs, the computational cost of their use grows factorially (even faster than exponentially) in the number of electrons that the molecule has. Therefore a great number of approximate methods strive to achieve the best trade-off between accuracy and computational cost. Presently computational chemistry can routinely and very accurately calculate the properties of the molecules that contain no more than, say, 10 electrons. The treatment of molecules that contain a few dozen electrons is practically feasible only by more approximate methods, such as DFT. There is some dispute within the field on whenether the latter methods are sufficient to accurately describe complex chemical reactions, such as those in biochemistry. A number of software packages that are self-sufficient and include many quantum-chemical methods are available. Among the most widely used are GAUSSIAN, GAMESS, Q-Chem, ACES, MOLPRO, DALTON, Spartan and PSI.

This article is adapted from from Wikipedia All Wikipedia article text is available under the terms of the GNU Free Documentation License

Essentials of Computational Chemistry : Theories and Models by Christopher J. Cramer

Structural Bioinformatics by Philip E. Bourne

Introduction to Computational Chemistry by Frank Jensen

Computational Chemistry (Oxford Chemistry Primers, 29) by Guy H. Grant

Computational Biochemistry and Biophysics by Oren M. Becker

Computational Color Science : Using MATLAB by Stephen Westland

A Handbook of Computational Chemistry: A Practical Guide to Chemical Structure and Energy Calculations by Tim Clark

The Emergence of Complexity in Mathematics, Physics, Chemistry, and Biology by Bernard Pullman

Computational Chemistry: Introduction to the Theory and Applications of Molecular and Quantum Mechanics by Errol G. Lewars

Computational Chemistry : A Practical Guide for Applying Techniques to Real World Problems by David Young

Computational Medicinal Chemistry for Drug Discovery by Patrick Bultinck

Computational Chemistry: Introduction to the Theory and Applications of Molecular and Quantum Mechanics by Errol G. Lewars

Structural Bioinformatics by Philip E. Bourne

Computational Methods in Physics, Chemistry and Biology : An Introduction by Paul Harrison

Reviews in Computational Chemistry by Kenny B. Lipkowitz


Recent Computational_chemistry related patents

From USPTO:
6706522: Plasmid DNA from Yersinia pestis
6699599: Deuterated semi-conducting organic compounds used for opto-electronic devices
6696426: Preservative free ophthalmic oxazolidinone antibiotic drug delivery systems
6692905: Methine compound-containing silver halide photographic emulsion and photographic material using the same
6689879: Modified HIV Env polypeptides
6689558: Cells for drug discovery
6686067: Deuterated semi-conducting organic compounds used for opto-electronic devices
6680203: Fourier transform mass spectrometry of complex biological samples
6680178: Profiling of protease specificity using combinatorial fluorogenic substrate libraries
6678619: Method, system, and computer program product for encoding and building products of a virtual combinatorial library
6678618: Neural network methods to predict enzyme inhibitor or receptor ligand potency
6677060: Deuterated semi-conducting organic compounds used for opto-electronic devices
6675103: Visualizing high dimensional descriptors of molecular structures
6673589: .alpha.-amylase mutants
6240374: Further method of creating and rapidly searching a virtual library of potential molecules using validated molecular structural descriptors
6230102: Computer system and process for identifying a charge distribution which minimizes electrostatic contribution to binding at binding between a ligand and a molecule in a solvent and uses thereof
6208942: Molecular hologram QSAR
6204232: .alpha.-amlase mutants
6188965: Apparatus and method for automated protein design
6185548: Neural network methods to predict enzyme inhibitor or receptor ligand potency
6185506: Method for selecting an optimally diverse library of small molecules based on validated molecular structural descriptors
6180654: Heterocyclic nonnucleoside inhibitors of reverse transcriptase
6178384: Method and apparatus for selecting a molecule based on conformational free energy
6174905: Cell differentiation inducer
6156759: Nonnucleoside inhibitors of reverse transcriptase, composite binding pocket and method for use thereof
6150415: Epoxide hydrolase complexes and methods therewith
6150088: Core structure of gp41 from the HIV envelope glycoprotein
6136748: Catalyst composition for the polymerization of olefins
6125235: Method for generating a refined structural model of a molecule
6124307: Nonnucleoside inhibitors of reverse transcriptase, composite binding pocket and methods for use thereof
6103711: Inhibitors of interleukin-1.beta. converting enzyme
6100380: Immunomodulating peptides and methods of use
6091492: Apparatus and method for determining the size distribution of particles by light scattering
6081766: Machine-learning approach to modeling biological activity for molecular design and to modeling other characteristics
6070127: Method of superposing molecular conformations of compounds
6054286: Methods to identify immunomodulators using cognate interaction of PKC-theta
6029114: Molecular modelling of neurotrophin-receptor binding
6025194: Nucleic acid sequence of senescence asssociated gene
6025147: Inhibitors of interleukin-1 .beta. converting enzyme
6022724: .alpha.-amylase mutants
6011119: Resin composition for electrophotographic toner, and toner
5269943: Method for treatment of soils contaminated with organic pollutants
5260882: Process for the estimation of physical and chemical properties of a proposed polymeric or copolymeric substance or material
4735651: Novel phytotoxic and plant growth regulating oligopeptide

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