Article:ksfams96

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@Article{ksfams96,
 author = {S. K. Kearsley and S. Sallamack and E. M. Fluder and J. D. Andose
           and R. T. Mosley and R. P. Sheridan},
 title = {{C}hemical {S}imilarity {U}sing {P}hysiochemical {P}roperty {D}escriptors},
 journal = {J. Chem. Inf. Comput. Sci.},
 year = {1996},
 volume = {36},
 pages = {118--127},
 abstract = {Similarity searches using topological descriptors have proved extremely
       useful in aiding large-scale screening. We describe alternative
       forms of the atom pair (Carhart et al. J. Chem. Inf. Comput. Sci.
       1985, 25,64-73.) and topological torsion (Nilakantan et al. J. Chem.
       Inf. Comput. Sci. 1987, 27,82-85.) descriptors that use physiochemical
       atom types. These types are based on binding property class, atomic
       log P contribution, and partial atomic charges. The new descriptors
       are meant to be more fuzzy than the original descriptors. We propose
       objective criteria for determining how effective one descriptor
       is versus another in selecting active compounds from large databases.
       Using these criteria, we run similarity searches over the Derwent
       Standard Drug File with ten typical druglike probes. The new descriptors
       are not as good as the original descriptors in selecting actives
       if one considers the average over all probes, but the new descriptors
       do better for several individual probes. Generally we find that
       whether one descriptor does better than another varies from probe
       to probe in a way almost impossible to predict a priori. Most importantly,
       we find that different descriptors typically select very different
       sets of actives. Thus it is advantageous to run similarity probes
       with several types of descriptors.},
  doi = {10.1021/ci950274j}
}