Performance in Structure Prediction

The multitude of different solutions that have been used for receptor-ligand scoring calls for an objective assessment that could help future users to decide which function to use under a given set of circumstances. To do this, one must differentiate between predicting protein-ligand complex structures 

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One of the goals of QSAR studies is to help explain retrospectively the response or property of a molecule with a rationale based on molecular structure. A second major goal and challenge of QSAR or QSPR studies is to develop models that are able to predict quantitatively the property of new molecules either real or virtual compounds. Thus, successful predictive QSAR models can have a tremendous impact in the design of new molecules. Furthermore, predictive models are useful to perform in silico predictions of the properties of new structures. In virtual screening, those molecules that are predicted to have the desired property according to the QSAR model are selected as best candidates. Reviews, examples, caveats, and modified versions of QSAR are described elsewhere (Kubinyi, 1997a,b Wermuth, 2008). Some recent examples reported in the food chemistry field are summarized in Table 2.4. 

Computational studies investigate reaction mechanisms and pathways by constructing potential energy profiles. This involves exploring reaction thermodynamics and kinetics, by examining reactants and products as well as the transition states geometries and activation energy barriers. Like those seen in structure prediction, most current studies implement effective core potentials and density functional theory to perform calculations.However, ECPs can be paired with MP2 to account for electron correlation thus far, this approach has only been used for smaller chemical systems. " Eurthermore, solvation methods such as the polarizable continuum model can be employed to examine... 

Today, there is quite a variety of such potentials [125-132], The performance of potentials for different tasks in structure prediction varies. Reviews and comparisons of database and molecular mechanics force fields as well an discussion of their controversial relationship to free energy can be found in [133-135],... 

Techniques for rapid evaluation of both catalyst structure and adsorbate structure under reaction conditions the dynamic rearrangement of the catalytically active surface should be correlated with catalytic performance in practice Predictive techniques for guiding and accelerating the development of catalysts for specific apphcations [3]... 

The lattice energies of the single-molecule crystals and co-crystal are assumed to be those for the most stable forms of each. To predict the stability of a given co-crystal it is therefore necessary to perform crystal structure prediction on each molecule independently and on the co-crystal itself. If it is necessary to predict which stoichiometry of co-crystal is the most stable, it is also necessary to perform crystal structure prediction on crystals with each possible stoichiometry. Since the cost and difficulty of a crystal structure prediction calculation increases considerably with the number of independent molecules in the asymmetric unit, this becomes a very hard problem which few have ventured to tackle. The role of crystal structure prediction is to identify the lowest energy structures that are possible for both co-crystal and its single-molecule crystalline components. The prediction of stoichiometry for a co-crystal requires consideration of all dissociation processes available for a given number of molecules in the asymmetric unit, for example ... 

We take a Bayesian approach to research process modeling, which encourages explicit statements about the prior degree of uncertainty, expressed as a probability distribution over possible outcomes. Simulation that builds in such uncertainty will be of a what-if nature, helping managers to explore different scenarios, to understand problem structure, and to see where the future is likely to be most sensitive to current choices, or indeed where outcomes are relatively indifferent to such choices. This determines where better information could best help improve decisions and how much to invest in internal research (research about process performance, and in particular, prediction reliability) that yields such information. 

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