Discovery Database

Ulrich CM, Bigler J, Velicer CM et al. Searching expressed sequence tag databases discovery and confirmation of a common polymorphism in the thymidylate synthase gene. Cancer Epidemiol Biomarkers Prev 2000 9 1381-1385. 

Ulrich, C. M., Bigler, J., Velicer, C. M., Greene, E. A., and Earin, F. M., Potter, J. D. (2000) Searching expressed sequence tag databases discovery and confirmation of a common polymorphism in the thymidylate synthase gene. Cancer Epidemiology, Biomarkers and Prevention. 9, 1381-1385. 

Direct property prediction is a standard technique in drug discovery. "Reverse property prediction can be exemplified with chromatography application databases that contain separations, including method details and assigned chemical structures for each chromatogram. Retrieving compounds present in the database that are similar to the query allows the retrieval of suitable separation conditions for use with the query (method selection). 

Nowadays a broad range of methods is available in the field of chemoinfor-matics. These methods will have a growing impact on drug design. In particular, the discovery of new lead structures and their optimization will profit by virtual saeening [17, 66, 100-103]. The huge amounts of data produced by HTS and combinatorial chemistry enforce the use of database and data mining techniques. 

S Wang, DW Zaharevitz, R Sharma, VE Marquez, NE Lewm, L Du, PM Blumberg, GWA Milne. The discovery of novel, stnicturally diverse protein kinase C agonists through computer 3D-database pharmacophore search. I Med Chem 37 4479-4489, 1994. 

Today, 3D databases, which provide the means for storing and searching for 3D information of compounds, are proven to be useful tools in drug discovery programs. This is well exemplified with the recent discovery of novel nonpeptide HIV-1 protease inhibitors using pharmacophore searches of the National Cancer Institute 3D structural database [13-15]. 

As the twentieth century came to a close, the job market for computational chemists had recovered from the 1992-1994 debacle. In fact, demand for computational chemists leaped to new highs each year in the second half of the 1990s [135]. Most of the new jobs were in industry, and most of these industrial jobs were at pharmaceutical or biopharmaceutical companies. As we noted at the beginning of this chapter, in 1960 there were essentially no computational chemists in industry. But 40 years later, perhaps well over half of all computational chemists were working in pharmaceutical laboratories. The outlook for computational chemistry is therefore very much linked to the health of the pharmaceutical industry itself. Forces that adversely affect pharmaceutical companies will have a negative effect on the scientists who work there as well as at auxiliary companies such as software vendors that develop programs and databases for use in drug discovery and development. 

Berlage, T. Analyzing and mining image databases Drug Discovery Today 2005 10 795-802. 

One early step in the workflow of the medicinal chemist is to computationally search for similar compounds to known actives that are either available in internal inventory or commercially available somewhere in the world, that is, to perform similarity and substructure searches on the worldwide databases of available compounds. It is in the interest of all drug discovery programs to develop a formal process to search for such compounds and place them into the bioassays for both lead generation and analog-based lead optimization. To this end, various similarity search algorithms (both 2D and 3D) should be implemented and delivered directly to the medicinal chemist. These algorithms often prove complementary to each other in terms of the chemical diversity of the resulted compounds [8]. 

Downs GM, Willett P. Clustering of chemical structure databases for compound selection. In van de Waterbeemd H, editor, Advanced computer-assisted techniques in drug discovery. Weinheim VCH Verlag, 1994. p. 111-30. 

Shi LM, Fan Y, Lee JK, Waltham M, Andrews DT, Scherf U, Pauli KD, Weinstein JN. Mining and visualizing large anticancer drug discovery databases. / Chem Inf Comput Sci 2000 40 367-79. 

Varady J, Wu X, Fang X, Min J, Hu Z, Levant B, Wang S. Molecular modeling of the three-dimensional structure of dopamine 3 (D3) subtype receptor discovery of novel and potent D3 ligands through a hybrid pharmacophore-and structure-based database searching approach. / Med Chem 2003 46 4377-92. 

Iwata Y, Arisawa M, Hamada R, Kita Y, Mizutani MY, Tomioka N, Itai A, Miyamoto S. Discovery of novel aldose reductase inhibitors using a protein structure-based approach 3D-database search followed by design and synthesis. J Med Chem 2001 44 1718-28. 

Rastelli G, Ferrari AM, Constantino L, Gamberini MC. Discovery of new inhibitors of aldose reductase from molecular docking and database screening. Bioorg Med Chem 2002 10 1437-50. 

Kurogi Y, Miyata K, Okamura T, Hashimoto K, Tsutsumi K, Nasu M, Moriyasu M. Discovery of novel mesangial cell proliferation inhibitors using a three-dimensional database searching method. J Med Chem 2001 44 2304-7. 

Gaines, B., The trade-off between knowledge and data in knowledge acquisition. In Knowledge Discovery in Databases (G. Shapiro and W. Frawley, eds.), p. 491. MIT Press, Cambridge, MA, 1991. 

Shapiro, G., and Frawley, W eds, Knowledge Discovery in Databases. MIT Press Cambridge, MA. 1991. 

Ghose, A. K., Viswanadhan, V. N., Wendoloski, J. J. A knowledge-based approach in designing combinatorial or medicinal chemistty libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases./. Comb. Chem. 1999, 1, 55-58. 

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