Topological Pharmacophores from 2DAligments

Generating a 2D-aligment, the precursor step of 2D pharmacophore elucidation consists in first establishing, for each atom of the aligned molecule, a list of possible matches34 (equivalent atoms) in the target compound (subsequently referred as the template). Unless the aligned compound is a close analogue of the template, this problem is not trivial, for certain atoms may not have any appropriate equivalents in the partner compound. Putatively matching atom pairs are vertices of similar...

Current Superpositioning Techniques for Aligning 3D Pharmacophores and Molecules

In the broad field of possible pharmacophoric alignment techniques one can distinguish between either point- or property-based approaches.62 With point-based approaches, atoms or chemical feature point distances are minimized, while property-based approaches generate a pharmacophore by assessing the molecular interaction potential (MIP) similarity, based on Goodford's GRID63 method, to generate alignments. Programs representing both approaches have been applied for the generation of...

Application of Pharmacophore Models in Virtual Screening

As shown in the previous section, there have been an impressive number of new approaches and tools appearing in the field of structure- and ligand-based pharmacophore modeling. All of them address slightly different issues and therefore a combination of several methods, as shown in different application examples, can significantly enhance the chances of success. In our study on the discovery of novel ligands for the sigma-1 receptor and related proteins we were able to find compounds binding to...

Validation of Models for Virtual Screening

A useful pharmacophore model must be able to correctly classify - and in the case of quantitative models, correctly predict the affinity of - a so-called test or validation set, which consists of active compounds that were not used during the generation of the model. Often the ability of the model to retrieve known actives from a larger drug-like database is also assayed. It has been confirmed by several studies that the characteristics of the known inactives or decoys chosen for VS assessments...

Atomcentered Fragments

Atom-Centered Fragments (ACF) consist of a single central atom surrounded by one or several shells of atoms separated from the central one by the same topological distance. This type of structural fragments was introduced in the early 1950s by Tatevskii,27'28'116 119 and then by Benson31 to predict some physicochemical properties of organic compounds in the framework of additive schemes. ACF fragments containing only one shell of atoms around the central one (i.e., atom-centered neighborhoods...

Similarity Search

The notion of molecular similarity (or chemical similarity) is one of the most useful and at the same time one of the most contradictory concepts in chemoinformatics.247,248 The concept of molecular similarity plays an important role in many modern approaches to predicting the properties of chemical compounds, designing chemicals with a predefined set of properties and, especially, in conducting drug design studies by screening large databases containing structures of available (or potentially...

Summary and Conclusion

The pharmacophore concept is a successful and well-known approach - both ligand- and structure-based - for drug design as well as for VS. Several methods for describing pharmacophores have been established, showing significant differences and capabilities in the way in which they describe chemical features as building blocks for pharmacophores. It is important that the chemical feature representation used reflects the interactions that are relevant for the target being represented, and some...

Qualitative vs Quantitative Pharmacophore Models

If a set of diverse molecules with measured affinities spanning multiple orders of magnitude is available for a given target, one can create pharmacophore models that can predict the binding affinity of the investigated compounds. The most widely-used method for this task is the Catalyst DiscoveryStudio module HypoGen.125 HypoGen tries to find models that are common among the active compounds of the training set but do not reflect the inactive ones. Pharmacophores that correlate best the...

References

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