Virtual compounds

An extensive series of studies for the prediction of aqueous solubility has been reported in the literature, as summarized by Lipinski et al. [15] and jorgensen and Duffy [16]. These methods can be categorized into three types 1 correlation of solubility with experimentally determined physicochemical properties such as melting point and molecular volume 2) estimation of solubility by group contribution methods and 3) correlation of solubility with descriptors derived from the molecular structure by computational methods. The third approach has been proven to be particularly successful for the prediction of solubility because it does not need experimental descriptors and can therefore be applied to collections of virtual compounds also. 

It is remarkable that only two descriptors were needed in this method. However, this equation is mostly only of historical interest as it is of little use in modem dmg and combinatorial library design because it requires a knowledge of the compound s experimental melting point which is not available for virtual compounds. Several methods exist for estimating log P [1-14], but only a few inroads have been made into the estimation of melting points. The melting point is a key index of the cohesive interactions in the solid and is still difficult to estimate. 

If structural information of the protein target is available, e.g., a crystal structure, in silico screening of huge virtual compound libraries can be conducted by the use of docking simulations. Based on identified primary hits, structural variations of the ligand can be evaluated by computational modeling of the ligand-protein complex. 

Anderson AC, Wright DL. The design and docking of virtual compound libraries to structures of drug targets. Curr Comp Aided Drug Des 2005 1 103-27. 

During the past five years, commencing with the publications of Lipinski and co-workers [1] and Palm and co-workers [2], a considerable amount of research has been performed in order to develop mathematical models for intestinal absorption in humans as well as other transport properties. The purpose of these investigations has been to develop computationally fast and accurate models for in silico electronic screening of large virtual compound libraries. 

The virtual compounds can be input into metabolism prediction programs, such as Metabolexpert [30] or Meteor [31], to identify principal pathways of expected drug metabolism. 

The virtual compounds can be screened against structural models of the metabolizing enzymes, including the known SNP variants. These procedures are becoming widely adopted for the cytochrome P450 isozymes involved in oxidative drug metabolism. 

Docking simulation is distinct from wet laboratory chemistry, where chemical reactions are performed using real rather than virtual compounds. The docking approach is more cost effective and efficient than the conventional chemical synthesis route. It allows a large database of virtual compounds to be screened and matched up with the binding site of the targeted protein. 

Fig. 7.3 Representation of interesting or promising cells. Each cube represents a cell in the partitioned descriptor space. The shaded cell contains one or more active compounds. The virtual compounds that fall into cells that are immediately adjacent to the interesting cells are also likely to be active, so one may add a degree of fuzziness to the design so as to capture these cells. One layer is shown, but the number of layers controls the focus or fuzziness of the region defined as interesting.

M.S. Virtual compound libraries a new approach to decision making in molecular discovery research. /. Chem. Inf Comput. Sci. 1998, 38, 1010-1023. 

Hits that are appealing to chemists as candidates for further optimization are often simple structures without excessive functionalizations this is due to the likelihood that complexity will increase during lead optimization [154]. A convenient way to gauge the potential attractiveness of a virtual library design is to compare molecular complexity scores of the virtual compounds to those calculated for hits that have been rated for their potential lead attractiveness by medicinal chemists. 

For the prioritization of virtual compound libraries based on biostructural information, usually a fixed target structure is assumed and the energetically minimized conformers... 

The iResearch Library is ChemNavigator s compilation of commercially accessible screening compounds. The database tracks over 21.7 million samples from around 150 vendors based on 14 miUion unique structures, including both physically available and virtual compounds. 

As stated before, PGVL is too large to be fully enumerated practically. Therefore our strategy is to find a way to focus in a just-in-time manner on much smaller sub-regions ( 104) of PGVL for subsequent on-the-fly enumeration followed by standard similarity search against the same query molecule. It is intuitively evident that a virtual compound space built from parallel synthesis reaction protocols has inherent array structures in the form of implicit arrays of related just-in-time enumerated compounds, even if those compounds do not have their molecular structures yet enumerated at the time this inherent array structure is exploited. 

Test Three For the third validation test, we selected 24 known drugs on the market as query molecules (see Fig. 13.5). This is a very realistic and challenging set in terms of diversity in their molecular structures and complexities required for their synthesis. The top 10 most similar virtual compounds to each query molecule were identified and plotted as color dots in Fig. 13.6 for both LEAPl and LEAP2. 

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