Pharmacophore prediction

Metabolism is still a barrier to be overcome. Some QSAR, pharmacophore, protein, and rule-based models are available to predict substrates and inhibitors of a specific cytochrome P450 isoenzyme [47-55]. 

The functionality available in MedChem Explorer is broken down into a list of available computational experiments, including activity prediction, align/ pharmacophore, overlay molecules, conformer generation, property calculation, and database access. Within each experiment, the Web system walks the user through a series of questions that must be answered sequentially. The task is then submitted to a remote server, where it is performed. The user can view the progress of the work in their Web browser at any time. Once complete, the results of the calculation are stored on the server. The user can then run subsequent experiments starting with those results. The Web interface includes links to help pages at every step of the process. 

Activity prediction is based on a list of models (i.e., QSAR models, pharmacophore models, etc.) that are maintained on the server. There is a second level of access so that only authorized users may be allowed to add or delete model entries. 

This method represents the most common and traditional application of computational tools to rational drug design. From a list of molecules of known activity, one can establish a 3D-pharmacophore hypothesis that is then transformed into a 3D-search query. This query is then used to search a 3D database for structures that fit the hypothesis within a certain tolerance. If the yield of active molecules is significant, then the query can be used to predict activities on novel compounds. In our situation, the enantiophore is built from the superposition of a list of sample molecules, which are all well separated on a given CSR Hence, the common features of this series of molecules can become a good enantiophore hypothesis for the enantiores-olution on this CSR... 

These pharmacophore techniques are different in format from the traditional pharmacophore definitions. They can not be easily visualized and mapped to the molecular structures rather, they are encoded as keys or topological/topographical descriptors. Nonetheless, they capture the same idea as the classic pharmacophore concept. Furthermore, this formalism is quite useful in building quantitative predictive models that can be used to classify and predict biological activities. 

CPU time. In response to these slow and rigorous calculations, many fast heuristic approaches have been developed that are based on intuitive concepts such as docking [10], matching pharmacophores [19], or linear free energy relationships [20], A disadvantage of many simple heuristic approaches is their susceptibility to generalization error [17], where accuracy of the predictions is limited to the training data. 

Li H, Sutter J, Hoffmann R. HypoGen an automated system for generating 3D predictive pharmacophore models. In Gtiner OF, editor. Pharmacophore perception, development and use in drug design., La Jolla International University Line, 2000. p. 173-89. 

Egnell AC, Houston JB, Boyer CS. Predictive models of CYP3A4 heteroactivation in vitro—in vivo scaling and pharmacophore modelling. J Pharmacol Exp Ther 2005 312 926-37. 

Smith PA, Sorich M, McKinnon R, Miners JO. QSAR and pharmacophore modelling approaches for the prediction of UDP-glucuronosyltransferase substrate selectivity and binding. Pharmacologist 2002 44 supplement. 

Smith PA, Sorich MJ, McKinnon RA, Miners JO. Pharmacophore and quantitative structure-activity relationship modeling complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity. J Med Chem 2003 46 1617-26. 

The phase transition boundaries (phase envelope) of adamantane need to be investigated and constmcted. Predictable and diverse geometries are important features for molecular self-assembly and pharmacophore-based dmg design. Incorporation of higher diamondoids in solid-state systems and polymers should provide high-temperature stability, a property already found in polymers synthesized from lower diamondoids. 

N GokaV et al. (1991) GABA-uptake inhibitors construction of a general pharmacophore model and successful prediction of a new representative. J Med Chem 34(8) 2547-2557... 

Attempts to build predictive models based on common pharmacophoric elements have had some modest success. In the most general sense, a basic nitrogen atom which is substituted by aromatic or otherwise hydrophobic groups is a clearly problematic motif [73,74]. However, there are many compounds which interact with hERG which do not contain these features, and newer pharmacophore models have been proposed... 

There have been numerous efforts in library synthesis to develop compounds with central nervous system (CNS) activity [37]. Most recently, a QSAR model has been developed based on the activity of 2500 compounds against 180 assays using proprietary 3D pharmacophore descriptors [38]. The model successfully predicted 83% of a test... 

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