Goals of Virtual Screening

It is important to note that none of the compounds identified in external databases as potent anticonvulsants and validated experimentally belongs to the same class of FAA molecules as the training set. This observation is very stimulating because it underscores the power of our methodology to identify potent anticonvulsants of novel chemical classes as compared to the training set compounds, which is one of the most important goals of virtual screening. 

As discussed in the introductory sections, the goal of virtual screening is to use computational tools together with the known 3D structure of the target to select a subset of compounds from chemical libraries for synthesis and... 

The basic goal of virtual screening is the reduction of the enormous virtual chemical space of small organic molecules, to synthesize... 

This entire book is devoted to cheminformatics and virtual screening - many of the chapters have discussed major cheminformatics concepts in the context of virtual screening. The main goal of this chapter is to highlight the applicability... 

A screening of the virtual combinatorial library was performed in several steps. As potential extractants must be insoluble in the water phase, the ISIDA-Log S module incorporating QSPR models for aqueous solubilities (log S)95 has been used for filtering the library. Thus, the compounds for which log S < -3 were considered insoluble in water other compounds were excluded. A subset containing 9,306 potentially insoluble molecules has been screened using the structure-log D models. The main goal of screening was the selection of potentially efficient extractants. However, in... 

Preliminary data on the selectivity issue are shown in Fig. 9.6. The virtual library comprised 1000 molecules from ACD (ACD v. 2000-1, Molecular Design) as described previously. Ten actives for FGF and CDK2 proteins were added to the random molecules to generate the virtual library for screening. The goal of the experiment was to isolate selectively FGF actives from the virtual library... 

A unique database is the GDB-13 database (49), which is an exhaustive enumeration of small-molecule structures containing up to 13 heavy atoms (restricted to C, H, N, O, S, P, and Cl). Although the database does not contain activity information associated with the structures, it can be used as a source of structures for virtual screening purposes (50). It is similar in nature to databases such as ZINC (51). The key difference is that the latter are all commercially available, whereas the former are completely virtual. This class of databases is useful primarily for virtual screening type methods, where the goal is to identify candidates for more in-depth study, rather than to explicitly understand SAR trends. 

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. 

The ultimate goals of the methods described in Section IV fall in the understanding of structure-activity relationships and the discovery of new molecules. SAR is an inherent part of the derivation of the models, whereas the virtual screening of databases against the models not only serves as a means of validation but also provides candidates with a better chance (compared to random selection) of having the desired property. 

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