Modeling Challenges

The accurate mathematical formulation of polymerizing flows is a challenging task for several reasons. For example, turbulent flows require complete resolution of all 

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Was the implementation ofthe new business model challenging in general t ... 

Fienberg, M.B., and J.P. Moore. 2002. AIDS vaccine models Challenging challenge viruses. Nat Med 8 207. 

Hubbard, R. E., Chen, I., and Davis, B. (2007). Informatics and modeling challenges in fragment-based drug discovery. Current Opinion in Drug Discovery, and Development 10, 289-297. 

R.E. Hubbard, I. Chen and B. Davis, Informatics and modelling challenges in fragment-based drug discovery, Curr. Opin. Drug Discov. Dev., 10, 289-297 (2007). 

Bioinformatics will play a vital role in overcoming this data integration and modeling challenge, because databases, visualization software, and analysis software must be built to enable data assimilation and to make the results accessible and useful for answering biological questions (p. 254). 

Maroudas, D., Modeling of radical-surface interactions in the plasma-enhanced chemical vapor deposition of silicon thin films, in (A.K. Chakraborty, Ed.), Molecular Modeling and Theory in Chemical Engineering , vol. 28, p. 252. Academic Press, New York (2001). Maroudas, D. Multiscale modeling, Challenges for the chemical sciences in the 21st century Information and communications report , National Academies, Washington, DC. p. 133. 

Artificial neural networks (ANN s) were effectively set aside for 15 years after a 1969 study by Minksy and Papert demonstrated their failure to correctly model a simple exclusive OR (XOR) function. i The XOR function describes the result of an operation involving two bits (1 or 0). A simple OR function produces a value of 1 if either bit or both bits have a value of 1. The XOR differs from an OR function in the output of an operation on two bits of value 1. The XOR function will yield a 0 while the OR function will yield a 1. Interest in ANN s resumed in the 1980 s after modifications were made to the layering of their neurons that allowed them to overcome the XOR test as well as a wide variety of other non-linear modeling challenges. 

Pure solid + fluid phase equilibrium calculations are challenging but can, in principle, be modeled if the triple point of the pure solid and the enthalpy of fusion are known, the physical state of the solid does not change with temperature and pressure, and a chemical potential model (or equivalent), with known coefficients, for solid constituents is available. These conditions are rarely met even for simple mixtures and it is difficult to generalize multiphase behavior prediction results involving even well-defined solids. The presence of polymorphs, solid-solid transitions, and solid compounds provide additional modeling challenges, for example, ice, gas hydrates, and solid hydrocarbons all have multiple forms. 

Although there are currently many analytical and modeling challenges with these data sets, further improvements in these technologies will engender applications in drug development, clinical research, and patient care. 

Diller, D.J. (2009) In silica hERG modeling challenges and progress. Current Computer-Aided Drug Design, 5, 106-121. 

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