Space chemical

A data set can be split into a training set and a test set randomly or according to a specific rule. The 1293 compounds were divided into a training set of 741 compounds and a test set ot 552 compounds, based on their distribution in a K.NN map. From each occupied neuron, one compound was selected and taken into the training set, and the other compounds were put into the test set. This selection ensured that both the training set and the test set contained as much information as possible, and covered the chemical space as widely as possible. 

Methods of analyzing the diversity of the selected subset ensure that an appropriate chemical space is covered. Descriptors such as fingerprints, and 2D, and 3D descriptors, as well as molecular surface properties, which can be... 

Even in this fiiirly diverse data set of structures, the dopamine and benzodiazepine agonists could be separated quite well only two neurons had collisions between these two types ol compounds. Even more importantly, however, we now know in which chemical space one would have to search For new lead structures for dopamine or for benzodiazepine agonists. 

The reliability of the in silico models will be improved and their scope for predictions will be broader as soon as more reliable experimental data are available. However, there is the paradox of predictivity versus diversity. The greater the chemical diversity in a data set, the more difficult is the establishment of a predictive structure-activity relationship. Otherwise, a model developed based on compounds representing only a small subspace of the chemical space has no predictivity for compounds beyond its boundaries. 

A major potential drawback with cluster analysis and dissimilarity-based methods f selecting diverse compounds is that there is no easy way to quantify how completel one has filled the available chemical space or to identify whether there are any hole This is a key advantage of the partition-based approaches (also known, as cell-bas( methods). A number of axes are defined, each corresponding to a descriptor or son combination of descriptors. Each axis is divided into a number of bins. If there are axes and each is divided into b bins then the number of cells in the multidimension space so created is ... 

Considering a median value of 106"1 possible structures in chemical space clearly indicates that the number of possible structures available is far too large for complete coverage by chemical synthesis and biological screening. It has been... 

There is evidence to suggest that drug-like structures exist in clusters in chemical space (privileged structures). Identification of these can greatly enhance success in screening. 

Complex optimization of the ligand-protein interactions require to scan large areas of the chemical space. Thus, the combinatorial chemist aims not at the preparation of single compounds but of chemical libraries. Chemical libraries can be produced as collections of single compounds or as defined mixtures. 

The challenge in the synthesis of chemical libraries is the vast number of different, potentially drug-like small molecules which is estimated to be as high as 1060. As all of these molecules can never be synthesized and tested, it is essential to define criteria for the composition of libraries spanning the biologically relevant areas of the chemical space most efficiently. An important criterion of a compound library is its chemical diversity, a term describing the similarity or dissimilarity of all library components. Thus, chemical diversity expresses how well a library represents all theoretical possibilities within the chemical property space. A library with low... 

Lipinski C, Hopkins A. Navigating chemical space for biology and medicine. Nature 2004 432 855-61. 

Partitioning or cell-based methods provide an absolute measure of the chemical space covered by a collection of compounds. They are based on the definition of a low-dimensional chemistry space, for example, one based on a small number of physicochemical properties such as molecular weight, calculated logP, and number of hydrogen bond donors [45]. Each property defines an axis of the chemistry-space. The range of values for each property is divided into a set of bins, and the combinatorial product of all bins then defines the set of cells or partitions that make up the space. 

To maximize safety and therapeutic efficacy, potential drugs are required to be highly specific for their protein target and orally bioavailable. In addition, for a drug candidate to reach the market, it must be patentably novel. A computational approach therefore needs to find novel compounds with well-defined pharmacological properties from the vast space of possible organic compounds ( chemical space ). 

Oprea TI. Chemical space navigation in lead discovery. Curr Opin Chem Biol 2002 6 384-9. 

The concept of property space, which was coined to quanhtahvely describe the phenomena in social sciences [11, 12], has found many appUcahons in computational chemistry to characterize chemical space, i.e. the range in structure and properhes covered by a large collechon of different compounds [13]. The usual methods to approach a quantitahve descriphon of chemical space is first to calculate a number of molecular descriptors for each compound and then to use multivariate analyses such as principal component analysis (PCA) to build a multidimensional hyperspace where each compound is characterized by a single set of coordinates. 

The E-state indices may define chemical spaces that are relevant in similarity/ diversity search in chemical databases. This similarity search is based on atom-type E-state indices computed for the query molecule [55]. Each E-state index is converted to a z score, Z =(% -p )/0 , where is the ith E-state atomic index, p is its mean and O is its standard deviation in the entire database. The similarity was computed with the EucHdean distance and with the cosine index and the database used was the Pomona MedChem database, which contains 21000 chemicals. Tests performed for the antiinflamatory drug prednisone and the antimalarial dmg mefloquine as query molecules demonstrated that the chemicals space defined by E-state indices is efficient in identifying similar compounds from drug and drug-tike databases. 

The atomic-level structural information encoded into the E-state generates a chemical space that can be efficient in QSAR modeling and in the virtual screening... 

While the mammals predominate in their integration and representation of the sensory world, their noses still tell the brain directly about its chemical space. To explain the workings of accessory olfaction, we need to trace the path of a signal molecule from the moment it leaves its source until a response occurs in the recipient. The events which occur en route will determine the effectiveness of the intended communication. 

There has been extensive work on computational modeling of ADME and safety properties in recent years, but the field is still evolving [102, 103]. There are two key limitations on the use of models for these endpoints. One limitation is technical - the quality and accuracy of the models for the chemical space of interest [104, 105]. 

Influence of chemical space on folding role of chaperones Multiple sites... 

Oprea, T., Zamora, I., Ungell, A.-L., Pharmacokinetically based mapping device for chemical space navigation,... 

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