Ensemble Discovery

Figure 8,8 Scheme for the construction of DNA-templated libraries and identification of active molecules after selection. Reprinte permission from Ensemble Discovery. 

Figure 8.12 Ensemble s integrated drug discovery platform based on DPC . Reprinted with permission from Ensemble Discovery.

Figure 8.13 Structures of the four macrocycle libraries prepared by Ensemble. Each library contains 5000 different structures. The total number of macrocycles in this series is about 20 000. Reprinted with permission from Ensemble Discovery.

Figure 8.15 Three-dimensional plot of DNA-templated library before and after selection. Each ball represents a unique compound structure in the library. After selection, hits are identified and the enrichment fold is represented by different colors. Reprinted with permission from Ensemble Discovery.

All the molecules in a DNA-encoded library are selected simultaneously. As shown in Ensemble Discovery and GSK s data analyses, a comprehensive view of SAR can be rapidly generated from the lines and planes formed in the SAR plot, high priority structural elements can be identified and applied to the next generation of library design, or to the synthesis of discrete compounds. In addition, a clear correlation between related structural elements with binding potency strongly validates the selection itself. 

We thank all the research groups and companies discussed above for their work to make the contents of this chapter possible. We thank Professor David R. Liu and Ensemble Discovery for providing figures and permissions. We thank Dr Nick Terrett and Dr. Frank Favaloro for their critical reading of the manuscript. 

The last work pertaining to the discovery of new catalysts is perhaps the most novel approach to be reported thus far. In one of the earliest approaches taken toward catalyst development, Menger et al. (61) attempted to find catalysts for phosphate ester hydrolysis. A series of eight functionalized carboxylic acids were attached to polyallylamine in various combinations. Each of these polymers were then treated with one of three metals, Mg2+, Zn2+, or Fe3+. The different members of each library were identified by the relative percentages of each carboxylic acid attached to the polyamine. For example, one polymer possessed 15% Oct, 15% Imi, 15% Phe, and 5% Fe3+. There is no attempt to identify the location of the various carboxylic acids in a given polymer. This approach is novel since each system consists of an ensemble of different ligands with the carboxylic acids positioned in various locations. Each polymer within a given ratio of carboxylic acids consists of a combinatorial library of potential catalysts. 

Galvani s discovery was followed nine years laterby that of his compatriot, Volta, who conununicated to the Royal Society in London an amazing thing If one used a pasteboard membrane to separate silver plates from zinc plates, and wetted the ensemble with salt water, an electric current flowed. Volta called his device the artificial electric organ. ... 

These results can be easily rationalized if ethane hydrogenolysis requires an ensemble of multiple Ni surface atoms to form an active site. The concentration of active ensembles will decline sharply with composition as the active Ni atoms are diluted with inactive Cu atoms in the surface.. Additional information on how bimetallic catalysts affect chemical reactions can be found in the excellent monograph by Sinfelt (J. H. Sinfelt, Bimetallic Catalysts Discoveries, Concepts, and Applications, Wiley, New York, 9st, . 

Elementary processes in chemical dynamics are universally important, besides their own virtues, in that they can link statistical mechanics to deterministic dynamics based on quantum and classical mechanics. The linear surprisal is one of the most outstanding discoveries in this aspect (we only refer to review articles [2-7]), the theoretical foundation of which is not yet well established. In view of our findings in the previous section, it is worth studying a possible origin of the linear surprisal theory in terms of variational statistical theory for microcanonical ensemble. 

X-ray diffraction is a tool for the investigation of the fine structure of matter. This technique had its beginnings in von Laue s discovery in 1912 that crystals diffract x-rays, the manner of the diffraction revealing the structure of the crystal. At first, x-ray diffraction was used only for the determination of crystal structure. Later on, however, other uses were developed, and today the method is applied not only to structure determination, but to such diverse problems as chemical analysis and stress measurement, to the study of phase equilibria and the measurement of particle size, to the determination of the orientation of one crystal or the ensemble of orientations in a polycrystalline aggregate. 

Since molecules are flexible and not static, a conformational analysis has to be carried out first to generate an ensemble of low-energy conformations. This is probably one of the most critical steps in the pharmacophore discovery process, since the goal is not only to consider the global miifima of a molecule, but also to include the bioactive conformation as part of an ensemble of low-energy conformations. 

A signal which has first vanished with time and then reappears some time later is called an echo. In spectroscopy, the echo is formally associated with a reversal of time, so that the reappearing signal can be understood in terms of time running backwards for a sufficiently isolated ensemble of molecules or spins (Fig. 2.2.9) [Bliil]. For uncoupled spins in simple liquids an NMR echo of the FID is generated by a 180° flip of the phase of all magnetization components. Since the discovery of the original two-pulse echo [Hahl], many other echoes (cf. Section 3.4) have been discovered in spectroscopy based... 

Property data from pharmaceutical prohling is not exclusively for optimizing PK. It can be considered as part of the multivariate ensemble of data (e.g., MW, chirality, purity, IC50, LD50) that is available to research teams for application to any drug-discovery experiment. 

Hong H, Tong W, Xie Q, Fang H, Perkins R. An in silico ensemble method for lead discovery decision forest. SAR QSAR Environ Res 2005 16(4) 339-347. 

Eanctot JK, Putta S, Lemmen C, Greene J. Using ensembles to classify compounds for drug discovery. J Chem Inf Comput Sci 2003 43(6) 2163-2169. 

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