Chemistry and Virtual Screening

For example, compounds from the pyridinylimidazole class of p38 inhibitors have served as leads for c-Raf [123] and AlkS [124]. For further details on the role of computational chemistry in kinase inhibitor structure-based design strategies and the range of computational tools being applied in this area see a recent overview by Woolfrey and Weston [89]. 

SiCHERi, F., Kuriyan, J., Curr. Opin. Struct. Biol. 1997, 7, 777-785. 

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29 Pargellis, C., Tong, L., Churchill, L., Cirillo, P.F., CiLMORE, X, Craham, 

Genevois, M., Gross , A., Zenatti, A., Campanacci, V, Cambiliau, C., Acta Crystallogr. D. Biol. Crystallogr. 2002, 58, 2109-2115. 

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Xue, L. and Bajorath, J. (2000) Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combin. Chem. High Throughput Screen. 3, 363-372. 

Descriptors in Chemoinformatics, Computational Combinatorial Chemistry, and Virtual Screening. 

The integration of combinatorial chemistry, structure-based library design and virtual screening [268, 269] also resulted in successful applications [270, 271]. It ultimately should result in broader SAR information about directionality and physicochemical requirements of acceptable building blocks. This concept is based on feasible scaffolds for exploring protein subsites using parallel or combinatorial synthesis. 

Since the introduction of combinatorial chemistry techniques, the emphasis has shifted from the design of large diverse libraries towards smaller more focused libraries. Indeed, as the number of protein crystal structures that are available has increased, so too has the interest in using this structural knowledge for combinatorial library design, compound acquisition programmes and virtual screening. This trend is likely to continue in the near future. 

Computational chemistry provides an array of valuable tools for in silico drug design and virtual screening, and it continues to expand its role in modern drug discovery. Computer molecular modeling, molecular graphic representations, and easy-to-access structural... 

Schuster, D., Maurer, E.M., Laggner, C., Nashev, L.G., Wilckens, T, Langer, T, and Odermatt, A. (2006) The discovery of new 1 Ibeta-hydroxysteroid dehydrogenase type 1 inhibitors by common feature pharmacophore modeling and virtual screening. Journal of Medicinal Chemistry, 49, 3454-3466. 

Vigers, G.P.A. and Rizzi, J.P. (2004) Multiple active site corrections for docking and virtual screening. Journal of Medicinal Chemistry, 47, 80-89. 

Keizers, P.H.J., van der Wijst, T, Jongejan, A., and Vemleulen, N.P.E. (2006) Catalytic site prediction and virtual screening of cytochrome P450 2D6 substrates by consideration of water and re scoring in automated docking. Journal of Medicinal Chemistry, 49, 2417-2430. 

Vainio, M.J., Kogej, T., and Raubacher, F. (2012) Automated recycling of chemistry for virtual screening and library design. Journal of Chemical Information and Modeling, 52, 1777-1786. 

The SPP plays a major role in chemical informatics since it provides a crucial link between the similarity of molecules and their corresponding bioactivities or properties. Wilkins and Randic formally described this principle, which now seems intuitively obvious, in a seminal paper published more than three decades ago [13]. Although similarity arguments had been advanced in chemistry before this time (of. [9]), none direotly addressed the stractural similarity between molecules in a computationally amenable form. In the late 1980s and the early 1990s, the SPP was reiterated [14, 15] and since that time has played a substantive role, explicitly or implicitly, in numerous studies associated with similarity searching and virtual screening. 

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