Compound databases comparison

Currunins D J, C W Andrews, J A Benfley and M Cory 1996. Molecular Diversity in Chemical Database Comparison of Medicinal Chemistry Knowledge Bases and Databases of Commercially Availabl Compounds Journal of Chemical Information and Computer Science 36 750-763. 

DJ Cummins, CW Andrews, JA Bentley, M Cory. Molecular diversity m chemical databases Comparison of medicinal chemistry knowledge bases and databases of commercially available compounds. I Chem Inf Comput Sci 36 750-763, 1996. 

The DFT/COSMO calculations are the rate-limiting part of the method and can easily take a few hours for molecules with up to 40 heavy atoms on a 3-GHz computer [36]. To overcome speed limitations, the authors developed the COSMOfrag method. The basic idea of this method is to skip the resource-demanding quantum chemical calculations and to compose a profiles of a new molecule from stored a profiles of precalculated molecules within a database of more than 40000 compounds. A comparison of the full and fragment-based versions for log P prediction was performed using 2570 molecules from the PHYSPROP [37]. RMSE values of 0.62 and 0.59 were calculated for the full COSMO and COSMOfrag methods, respectively [36]. 

Electronic databases of the mass spectral fragmentation patterns of known molecules can be rapidly searched by computer. The pattern and intensity of fragments in the mass spectrum is characteristic of an individual compound so comparison of the experimental mass spectrum of a compound with those in a library can be used to positively identify it, if its spectrum has been recorded previously. 

Cummins, D.J., Andrews, C.W., Bentley, J.A. and Cory, M. Molecular diversity in chemical databases comparison of medicinal chemistry knowledgebases and databases of commercially available compounds. J. Chem. Inf. Comput. Sci., 1996,36,750-763. 

Much of our current knowledge of bonding geometry of atoms has been derived from crystal structure determinations. Atomic coordinate data that have been published are included in databases, as will be described in more detail in Chapter 16. For organic compounds the Cambridge Structural Database " can be accessed by computer. Similar data for inorganic compounds are found in the Inorganic Crystal Structure Database. Comparisons with other experimental methods, such as... 

Medina-Franco, J. L., Maggiora, G. M., Gulianotti, M. A., Pinilla, C., and Houghten, R. A. (2007). A similarity-based data-fusion approach to the visual characterization and comparison of compound databases. Chem. Biol. Drug. Des. 70,393-412. 

Next to ADME phenomena, recent data mining studies also focused on the development or improvement of models predicting physicochemical properties relevant to the field of ADME. Examples are Henry s law constant [92], polar surface area [93], and log P [94]. These models try to overcome limitations of already existing models, see for example SlogP [94] vs. Clogp [95], or aqueous solubility [96], The latter study used more than 2000 compounds selected from the AQUASOL [97] and PHYSOPROP [98] databases. Comparison with a multilinear regression showed clear preference for the neural network. 

The combination of fusion rules (e.g., maximum and mean-fusion) gave rise to the multi-fusion similarity (MFS) maps that were developed for the visual characterization and comparison of compound databases [139]. This approach has been employed to explore SARs of compound datasets [140] and to compare combinatorial libraries [141]. Consensus approaches are also applied in diversity analysis of compound collections complementary 2D and 3D representations are used to obtain a comprehensive characterization of the diversity of large compound databases [60, 142]. 

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