Chemical virtual screening

Savchuk NP, Balakin KV. Data mining approaches for enhancement of knowledge-based content of de novo chemical libraries. In Alvarez H, Shoichet B, editors, Virtual screening in drug discovery. New York CRC Press, 2005. p. 121-49. 

NMR 3D structure of the undecapeptide U-II generated a ligand pharmacophore hypothesis that served as query for the virtual screening of the Aventis in-house compound repository. Active leads from six different chemical classes could be identihed by the 3D search, for example, compound 17 (ECso = 400nM Fig. 16.2) [91]. 

Bissantz C, Folkers G, Rognan D. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem 2000 43 4759-67. 

Shoichet BK. Virtual screening of chemical libraries. Nature 2004 432 862-5. 

The atomic-level structural information encoded into the E-state generates a chemical space that can be efficient in QSAR modeling and in the virtual screening... 

All of these parameters (with the possible exception of SAP) are frequently used in QSAR studies or as filters in virtual screening. The SAP descriptor was included to check for correlations between PSA and quantum chemically calculated charges. 

Roche, O., Trube, G Zuegge, J., Pflimlin, P., Alanine, A. and Schneider, G. (2002) A virtual screening method for prediction of the HERG potassium channel liability of compound libraries. Chembiochem A European Journal of Chemical Biology, 3, 455-459. 

The rather time- and cost-expensive preparation of primary brain microvessel endothelial cells, as well as the limited number of experiments which can be performed with intact brain capillaries, has led to an attempt to predict the blood-brain barrier permeability of new chemical entities in silico. Artificial neural networks have been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those of the blood from their structural parameters [117, 118]. A summary of the current efforts is given in Chap. 25. Quantitative structure-property relationship models based on in vivo blood-brain permeation data and systematic variable selection methods led to success rates of prediction of over 80% for barrier permeant and nonper-meant compounds, thus offering a tool for virtual screening of substances of interest [119]. 

Perola, E., Xu, K., Kollmeyer, T.M., Kaufmann, S.H., and Prendergast, F.G. Successful virtual screening of a chemical database for farnesyl-transferase inhibitor leads./. Med. 

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