3D pharmacophore searching

The JME can also serve as a query input tool for structure databases by allowing creation of complex substructure queries (Figure 2-130), which are automatically translated into SMARTS [22]. With the help of simple HTML-format elements the creation of 3D structure queries is also possible, as were used in the 3D pharmacophore searches in the NCI database system [129]. Creation of reac-... 

The 3D pharmacophore search with C(5)ROL in the Biochemical Pathways database provided 13 different molecules as hits. To further limit the number of hits, the additional restriction was imposed that the hits should have only two hydrogen... 

Recent examples of successful peptide-ligand based discoveries of drug-like peptidomimetics include the discovery of SST antagonists, or the discovery of non-peptidic antagonists of the recently deorphanized urotensin II receptor at Sanofi-Aventis. ° As illustrated in Fig. 3, Flohr etal. used 3D models of the NMR solution structure of cyclic peptide derivatives of Urotensin II as a template for virtual 3D pharmacophore searches which resulted into non-peptidic candidates for lead optimization. 

Some hits also revealed sufficient selectivity of type 1 inhibition versus the type 2 isoform, which is advantageous for the side-effect profile of these compounds. Comparison of the model for llp-HSDl inhibitors with the X-ray crystal structure (which was published shortly after model generation and VS) showed good correlation of the chemical features responsible for ligand binding. In another study, a combination of common feature-based qualitative and quantitative models was used as 3D pharmacophore search query to successfully detect novel endothelin-A antagonistic lead structures. 

Figure 9.3. MACCS— the Molecular ACCess System—an early structure indexing system. This program originally used fixed menus for searching, registration, and reporting. Later versions allowed users to customize the menus. The figure shows the result of a 3D pharmacophore search for ACE inhibitors. Out of a database of 115,000 structures, 21 fit the 2D and 3D requirements of the search query. The user could typically browse the "hits" from the search, save the list of structures to a list file, and output the structures to a structure-data file (SDFile). The MACCS database was a proprietary flat database system in which data of a given type, say, formula, was stored in a given file, indexed by the compound ID number.

The Unity 3D database system, which features rapid flexible 3D pharmacophore searching... 

In this chapter, we will start by providing an overview of the evolution of the 3D pharmacophore concept and subsequently show the usefulness of 3D pharmacophore searching in modern lead discovery. The use of this approach for combinatorial library design, compound classification, and molecular diversity analysis is presented. Examples of successful applications reported in the last couple of years are reviewed. 

Substructure and 3D pharmacophore searching involve the specification of a precise query, which is then used to search a database in order to identify molecules for screening. In such an approach, either a molecule matches the query or it does not Similarity searching offers a complementary approach, in that the query is typically an entire molecule. This query molecule is compared to all molecules in the database and a similarity coefficient calculated. The top-scoring database molecules (based on the similarity coefficient) are the hits from the search. In a typical scenario the query molecule would be known to possess some desirable activity and the objective would be to identify molecules which will hopefully show the same activity. We therefore require some method for deciding how to compute the similarity between two molecules. In order to achieve this we need to choose a set of molecular descriptors for the compounds. These descriptors are then used to compute the similarity coefficient. 

In contrast to a 2D search, the structural graph of the molecules does not account for a match of two molecules in 3D pharmacophore searching. 

Besides the use of pharmacophore fingerprints, 3D pharmacophore searches are among the most prominent 3D virtual screening techniques. Here distances between pharmacophoric features of the reference structures are compared with the respective distances in the structures of the search database. Again, these distances are conformation dependent and may be precomputed for a number of low-energy conformers in the search database to speed up the virtual screening process. This important approach is discussed in detail in a separate chapter of this book. 

A further important option for ligand-based virtual screening is to perform shape comparisons that have a pronounced scaffold hopping potential. An often used method of this kind is ROCS [31] (OpenEye Scientific Sofiware, 9 Bisbee Court Suite D, Santa Fe, NM 87508, USA. Available at http //www.eyesopen.com, March 20, 2009). ROCS employs continuous functions that are derived from atom-centered Gaussians to calculate the volume overlap between two 3D structures. The use of Gaussians drastically speeds up the computational process, and ROCS is able to search even databases with millions of compounds for molecules that can adopt shapes similar to the reference compound. As in the case of 3D pharmacophore searches, the low-energy conformations must be precomputed for the search database. 

Figure 12.13 Ligand-based virtual screening examples for CPCRs. (a) NPY5 receptor antagonist from 3D pharmacophore searching and chemical optimization, IC50 2.8 nM.

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