Pharmacophore analysis

The generation of a diverse set of compounds based on analysis of the pharmacophores they contain is rather more complex, requiring the generation of a pharmacophore fingerprint for all conformations of every molecule in the database. The pharmacophore concept discards the traditional perceptions of chemical structure, 

Optimal Diverse Subset Mean Tanimoto = 0.73 Diverse Subset vs Commercial Database Mean Tanimoto = 0.4 

Chart 1 Graphical example of self-similarity within a database and comparison of a diverse subset with an external commercial database 

The number of descriptors commonly used in a pharmacophore analysis is refreshingly small however, this advantage can be offset by the huge numbers of conformations that flexible molecules are able to adopt. The commonly used descriptors include  

In addition to these, the following customisable descriptors may be added  

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Hence there are multiple solutions for the final set of 10000 compounds. The final selection can be diversity driven using for example cluster analysis based on multiple fingerprints [63], hole filling strategies by using scaffold/ring analysis (LeadScope [66], SARVision [66]) or pharmacophore analysis [67, 68]. For a review of computational approaches to diversity and similarity-based selections, see the paper of Mason and Hermsmeier [69] and the references therein. 

M. H. and Willson, T.M. (2001) Pharmacophore analysis of the nuclear oxysterol receptor LXRa. Journal of Medicinal Chemistry, 44, 886-897. 

Many other approaches have been and are being developed for computeraided design of inhibitors. For example, pharmacophore analysis can identify the spatial arrangement of groups or atoms common to all active inhibitor molecules and then incorporate these elements into a single molecule [127,128]. 

Dr. Shi-Yi Yue joined the Astra Research Centre in Montreal in 1995 as a computational chemist. The Astra computational chemistry effort started with pharmacophore analysis and G-protein coupled receptor modeling and has... 

After performing pharmacophore analysis on a set of compounds, typically the user will have to select the model(s) with biological and/or statistical relevance, often from multiple possible solutions and use for further research purposes. 

Using all compounds chosen to participate in a pharmacophore analysis, a molecular spreadsheet can be created and the user can manually select the molecules that will belong to the set that will define the reference pharmacophore space (active set). 

Three-point pharmacophores have traditionally been used for many applications but have recently been more and more replaced by four-point pharmacophores (Mason et al. 1999), which increases the complexity of the search but also the resolution of the pharmacophore analysis. This is the case because the additional point increases the total number of inter-point distances from three for a three-point pharmacophore to six for a four-point pharmacophore. Pharmacophore searching is further refined by assigning alternative features to each point (e.g., hydrogen bond acceptors, donors, or charged groups) and ranges to inter-point distances (rather than an exact distance). For example, five different features (e.g., atom types or groups) may be permitted for each point... 

Several papers proposing multiple recognition sites for Pgp have been presented in the past. In this work, the pharmacophoric analysis of the dataset shows that the requirements to interact with Pgp are the same for all 129 compounds. Since in our database we have not included known R-site binders and anthracydines, we cannot say definitively that the pharmacophore found represents one of the binding sites that have been described in the literature. Two of the molecules present in the database, verapamil and dipyridamole, are known to bind in the H-site described by Shapiro and Ling [10]. We also cannot definitely state that the pharmacophore defines any functional site within the transporter. Further work, to try to define the location of the corresponding amino acids in a protein homology model, is in progress. 

Exploration of available chemical space Advanced medicinal chemistry approaches Broader selectivity studies Detailed pharmacophore analysis Results from in vitro ADME/T studies PK and PD characteristics... 

This approach was based on the application of the pharmacophore analysis software, Alanet-II, developed at Alanex. This software was designed to deal specifically with flexible molecules, so that peptide or peptide-like analogs of up to ten amino acids in length could be analyzed. The high flexibility of peptide-like structures presented a... 

Scheme 3) (13). All these leads were identified using structural information gathered from extensive work on peptide leads and a computational pharmacophore analysis design approach. Much attention and focus has been placed on cyclic ureas and dihydropyranones. The research efforts on cyclic urea derivatives resulted in some of these inhibitors being selected for clinical evaluation (12-15, Schemed) [13]. The research on dihydropyranones resulted in the recent approval of Tipranavir (7) for HIV treatment, which is given in combination with Ritonavir (3) [46,47].

Vassiliev PM, PerElova VN, Tyurenkov IN (2008) ComparaEve pharmacophore analysis of antiischemic acEvity of known drugs and molecular complexes of GABA derivaEves. Bull Volgogr Res Cent RAMS 3 73-75... 

Pharmacophore analysis can be very fast. For example, to check a library of 1000000 conformers against a three-point pharmacophore model can be done pn a typical workstation in a few minutes. Of course, as we have already discussed, the 3D structures of each conformation must first be generated, which can be very slow. 

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