Structures Databases Chemical -. Merck

The examples from SciFinder and the Merck Index are not intended to question the quality of these products, which we consider to be outstanding. They are invaluable resources to many chemists worldwide, and the error rate in these two databases is insignificant if one takes into account the enormous volume of indexed data. One of us has published a structure-activity paper on HIV-protease inhibitors [31] where a modified peptide was present in both the training set and the test set. Al Leo of Pomona College has recently [32] detected 100 chemical and name errors in the printed version of the sixth edition of Burger s Medicinal Chemistry [33], errors that will be corrected in the on-line edition [34]. One can never be too careful in verifying the available information, in particular if one is to invest a significant amount of resources in that area. 

An identical approach was used by Branca et al. [16] at Merck and led to the identifcationofanovel inhibitor of poly(ADP-ribose) polymerase-1 (PARPl). The 2D descriptors were similar to the CATS all the two-point pharmacophores in each molecule were derived from all possible atom pairs and described in terms of atom types, number of Jt electrons, number of heavy atoms attached, and number of covalent bonds separating the two atoms along the shortest path. Known PARPl inhibitors were collected from patents, publications, and publicly available databases and used to train a support vector machine (SVM) classifier. A SVM constructs the plane that best separates active and inactive compounds in the multidimensional space defined by the molecular descriptors. The results of the SVM were used to classify the compounds in the Merck collection and those predicted to be active were screened. One compound was particularly potent and was chosen as the starting point for a structure-activity relationship (SAR) exploration of this chemical class. Docking studies on the PARPl crystal structure were used to guide the synthetic efforts. 

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