Pharmacophores analysis tools

Constrained Search and the related approach of SCAMPI both integrate the conformational analysis closely with the pharmacophore discovery. This has the advantage that the sampling of conformational space can be more focused on key regions. With both Catalyst and DANTE, conformational analysis was explicitly kept separate, in the latter to allow one to take advantage of any innovations in conformational analysis tools. And, indeed, there continues to be a steady flow of new approaches in conformational analysis—pharmacophore discovery is critically dependent on high-quality exhaustive conformational analysis. Based on our experience thus far, we cannot conclude that either approach is superior (integrated vs. external). Furthermore, a consensus has not yet been reached on the optimal manner to perform conformational search as needed by pharmacophore discovery. This will continue to be a fruitful area of research. 

Sanders MPA, Barbosa AJM, Zaizycka B, Nicolaes GAP, Klomp JPG, de Vlieg J, Del Rio A (2012) Comparative analysis of pharmacophore screening tools. J Chem Inf Model 52 1607-1620... 

An alternative metric to describe 3-D properties of molecules is discussed by Ashton et al.26 In their approach, a pharmacophore fingerprint is used in conjunction with conformational searching to determine possible 3-D shapes that molecules can adopt. Tools from Tripos and CDL are available to carry out this type of analysis. However as with other methods there are limitations, the completeness of conformational searching being one. Perhaps the most important limitation of the approach is that it has a tendency to pick the most flexible molecules (that set the most pharmacophore bits). In a lead discovery experiment, following up on flexible molecules can be a long and sometimes fruitless process. 

The kinds of calculations described above are done for all the molecules under investigation and then all the data (combinations of 3-point pharmacophores) are stored in an X-matrix of descriptors suitable to be submitted for statistical analysis. In theory, every kind of statistical analysis and regression tool could be applied, however in this study we decided to focus on the linear regression model using principal component analysis (PCA) and partial least squares (PLS) (Fig. 4.9). PCA and PLS actually work very well in all those cases in which there are data with strongly collinear, noisy and numerous X-variables (Fig. 4.9). 

After this general preselection, it can be advantageous to apply further steps of hierarchical filtering. As mentioned above, this could involve the selection of functional groups inevitably required to anchor a ligand to the most prominent interaction sites. Subsequently, the information of the "hot spot" analysis—translated into a pharmacophore hypothesis—can be used as matching criterion for a fast database screen. Such tools either involve fast tweak searching (355)or scan over precalculated conformers of the candidate molecules (356). The list of prospective... 

Abstract The aim of the present chapter is to present the current research and potential applications of chemoinformatics tools in food chemistry. First, the importance and variety of molecular descriptors and physicochemical properties is delineated, and then a survey and chemical space analysis of representative databases with emphasis on food-related ones is presented. A brief description of methods commonly used in molecular design, followed by examples in food chemistry are presented, such methods include similarity searching, pharmacophore modeling, quantitative... 

Horvath D. ComPharm—automated comparative analysis of pharmacophoric patterns and derived QSAR approaches, novel tools in high-throughput drug discovery. A proof-of-concept study applied to farnesyl protein transferase inhibitor design. In Diudea MV ed. QSPR/QSAR Studies by Molecular Descriptors. Huntington, NY Nova Science Publishers, 2001 389-433. 

CDK Tavema is an open-source tool. CDK Taverna can be used to create chemical workflows. Recurring tasks can be automated using CDK Tavema. This can be applied for chemical data filtering, transformation, curation, migrating workflows, chemical documentation and information retrieval-related workflows (structures, reactions, pharmacophores, object relational data etc.), data analysis workflows (statistics and clustering/machine learning for QSAR, diversity analysis etc.) [17] (Fig. 9.4). 

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