Alexander D. MacKerrell of the University of Maryland at Baltimore, Sudhakar V. Pamidighantam of the University of Illinois at Urbana-Champaign, Adrian E. Roitberg of the University of Florida, and John W.D. Connolly and Michael Sheetz of the University of Kentucky are supported by the NSF Division of Chemistry under the Cyberinfrastructure and Research Facilities Program. This project will create Cyberenvironments to automate the process of parameterization for classical molecular mechanics (MM) and semi-empirical (SE) Hamiltonians and allow for wide dissemination of the developed parameters. Project goals include an extensible cyberenvironment for i) the rapid and systematic parameterization of novel Hamiltonians and ii) the systematic extension of currently available models, with the resulting parameters sets from both i) and ii) to be made available via the cyberenvironment. The integrated environment will include a database of experimental and quantum mechanical reference data to be used in the parameterization process along with computational resources for data acquisition, automatization of QM reference data generation and automatization of parameter optimization processes. GridChem Computational Chemistry Grid will serve as the computational cyberinfrastructure for Quantum Chemical and Molecular Mechanics services and will include workflows from which specific parameterization schemes will be created and executed. Workflow management tools specific to generating QM reference data, monitoring parameter optimization and analysis will be implemented and will include interfaces allowing for expert intervention in the parameterization process. Many existing popular MM and SE Hamiltonians will be integrated from which a wide range of parameters encompassing biological, organic and inorganic species will be accessible for direct use or further optimization. The infrastructure will be extensible in terms of data sources and energy functions allowing for its applicability in any parameterization scheme.
This project will provide for more accurate descriptions of the static and dynamic properties of a wide range of material, pharmacological and biological systems using theoretical methods by simplifying the task of parameter optimization. This will allow for the generation of high quality parameters for a wide variety of molecular systems. Improvements in the accuracy of modeling as well as the range of accessible chemical systems will benefit such fields as chemistry, nanotechnology, medicine and biology, among others. In addition, analytical models used in engineering fields such as structural mechanics and fluid dynamics will become accessible to molecular level treatments.
