SAS maps

Structure-activity similarity (SAS) maps, first described by Shanmugasundaram and Maggiora (35), are pairwise plots of the structure similarity against the activity similarity. The resultant plot can be divided into four quadrants, allowing one to identify molecules characteristic of one of four possible behaviors smooth regions of the SAR space (rough), activity cliffs, nondescript (i.e., low structural similarity and low activity similarity), and scaffold hops (low structural similarity but high activity similarity). Recently, SAS maps have been extended to take into account multiple descriptor representations (two and three dimensions) (36, 37). In addition to SAS maps, other pairwise metrics to characterize and visualize SAR landscapes have been developed such as the structure-activity landscape index (SALI) (38) and the structure-activity index (SARI) (39). 

SAR Index. The SAR Index (SARI) has recently been introduced to quantitatively capture the continuous, discontinuous, or heterogeneous nature of activity landscapes and SARs. Similar to SAS maps, it exclusively relies on the 2D structural similarity and potency distribution within a set of active compounds. However, SARI aims to categorize the SARs of a population of compound sets without employing idealized reference states. It generates a numerical index between 0 and 1 that reflects the (dis-)continuity of the SARs under consideration. SARI distinguishes between three major categories of... 

A particularly useful way toward a graphical representation of chemical similarity versus biological similarity was introduced using the so-called structure-activity similarity (SAS) maps [104], These maps provide a graphical and numerical tool for the analysis of SAR on their x-axis, the chemical similarity of compound pairs is plotted versus the biological similarity on the y-axis. Based on the pairwise comparison of chanical structure and biological activity in a compound series, regions with different SAR characteristics can be identified. 

Because chemical spaces tend to be multidimensional, they cannot be represented graphically. This has led to the development of simple 2D SAS [183] and related maps [140-142,184-187]. SAS maps typically represent stractural similarity values along the abscissa of the map and activity similarity values, given by... 

AAct(t /) I and vice versa, as illustrated in Figure 15.10, which provides a schematic representation of a prototypical SAS map. 

Figure 15.11 depicts an SAS map for a set of 299 norepinephrine transporter inhibitors (44,551 data points). In the figure (cf. Figure 15.10b), the ordinate indicates... 

SAS maps have also been used to represent consensus models of activity landscapes by means of fusing similarity measures obtained from different 2D and 3D molecule representations and similarity functions [145,189,191]. In addition, SAS maps were recently adapted to model multitarget activity landscapes by representing in one axis the activity similarity of compound datasets screened across multiple biological endpoints [192]. SAS maps have been extended to characterize property landscapes other than activity landscapes. For example, stmcture-flavor similarity maps have been proposed to systematically characterize stmcture-flavor associations of a comprehensive flavor database [182]. A number of additional types of 2D MFS maps that characterize a different fusion-based similarity on each axis have also been developed and are described in Section 15.5.3. 

In general, the advantages of using an automated method may be comparable to those of SA refinement in X-ray crystallography [68], where many of the operations necessary to refine a structure can be done automatically and the remaining manual interventions are easier because the SA refinement usually results in a more easily interpreted electron density map. Automated methods are usually used in combination with manual assignment. However, fully automated assignment of the NOEs is possible (see Eig. 7) [69]. 

The lower a graph is more interesting. While initially the Poincar6 phase portrait looks the same as before (point E, inset 2c) an interval of hysteresis is observed. The saddle-node bifurcation of the pericxiic solutions occurs off the invariant circle, and a region of two distinct attractors ensues a stable, quasiperiodic one and a stable periodic one (Point F, inset 2d). The boundary of the basins of attraction of these two attractors is the one-dimensional (for the map) stable manifold of the saddle-type periodic solutions, SA and SB. One side of the unstable manifold will get attract to one attractor (SC to the invariant circle) while the other side will approach die other attractor (SD to die periodic solution). 

Figure 2. Restriction map of the A. aculeatus rhgA genomic clone plM803. pIM803 contains a 3.9-kb chromosomal Ba/nHI-Sa/1 fragment in pBluescriptSK(-l-). The open reading frame is indicated as closed bars. 5 - and 3 - untranslated sequences and introns are given as open boxes.

Figure 1.1. Outline index map of the Japanese subduction zones. Thick lines with teeth are converging plate boundaries. Arrows indicate relative plate motions. Abbreviations su, Suruga trough sa, Sagami trough sf, South Fossa Magna triple junction och, Off Central Honshu triple junction ISTL, Itolgawa-Shizuoka Tectonic Line KSM, Kashima VLBl station (Uyeda, 1991).

Beginning with SAS 9.1, the LIBNAME statement can be used to simply map to an Excel or Access database. This facility is available in the Microsoft Windows and UNIX operating systems. For example, the following SAS code reads in and then prints the lab normal file normal ranges.xls. 

Because the XML map file is valid XML itself, you can read how the SAS variables are translated from the XML lab normals data file. Once the XML map file is defined, you just need the simple SAS program that follows to read the lab normals XML file into SAS. 

Beginning with SAS 9.1, the SAS XML Mapper (previously known as Atlas) is a SAS stand-alone Java application supplied as part of Base SAS. The SAS XML Mapper is a graphical user interface assistant for building SAS XML Map files. Here is a display that shows the previous example s XML Map file being built from within the XML Mapper application ... 

There are a couple of items to note about MedDRA and the preceding sample SAS code. First, the example assumes that you have already managed to read the system organ class, preferred term, and lowest-level term data tables from the MedDRA dictionary into SAS. Second, the preferred terms may map to more than one system organ class. In this case, MedDRA provides a variable at the preferred term level to indicate what the primary system organ class is. So, you may need to subset the preferred term data set in the example in order to select just the primary system organ class. 

Maps Made Easy Using SAS by Mike Zdeb... 

The effects of the autonomic nervous system on MAP are summarized in Figure 15.4. The parasympathetic system innervates the SA node and the AV node of the heart. The major cardiovascular effect of parasympathetic stimulation, by way of the vagus nerves, is to decrease HR, which decreases CO and MAP. 

Fig. 2.3 The solvent accessible surface (SAS) area corresponds to that mapped out by the center of a sphere representing the solvent molecule (gray) as it is rolled over the van der Waals surface of the solute (light gray). In the COSMO model, the SAS is then divided into a series of segments of area S and charge density cr, centered at a position R(j. ...

In order to apply the SA protocol, one of the keys is to design a mathematical function that adequately measures the diversity of a subset of selected molecules. Because each molecule is represented by molecular descriptors, geometrically it is mapped to a point in a multidimensional space. The distance between two points, such as Euclidean distance, Tanimoto distance, and Mahalanobis distance, then measures the dissimilarity between any two molecules. Thus, the diversity function to be designed should be based on all pairwise distances between molecules in the subset. One of the functions is as follows ... 

Carr SA, Annan RS, Huddleston MJ. Mapping posttranslational modifications of proteins by MS-based selective detection Application to phosphoproteomics. In Burlingame AL, ed.. Mass Spectrometry Modified Proteins and Glycoconjugates, Vol. 405, New York Academic Press, 2005, 82-115. 

Chen Y, Blom IE, Sa S, Goldschmeding R, Abraham DJ, Leask A. CTGF expression in mesangial cells involvement of SMADs, MAP kinase, and PKC. Kidney International 2002, 62, 1149-1159. 

Tishkoff SA, Verrelli BC. Role of evolutionary history on haplotype block structure in the human genome implications for disease mapping. Curr Opin Genet Dev 2003 13 569-575. 

Figure 16.3 Maps representing the Regional Standardized Mortality ratio (SMR-REG) distribution for each ASL referred to bronchus, trachea, and lung neoplasm cancer, for male (A) and female (B) in Campania region. NA, AV, BN, CE, and SA prefix in the labels indicates the pertinence of an ASL to a provincial territory (NA = Naples AV = Avelhno BN = Benevento CE = Caserta SA = Salerno).

---