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  author = {James L. Melville, and Jonathan D. Hirst},
  title = {TMACC: Interpretable Correlation Descriptors for
                 Quantitative Structure-Activity Relationships}, 
  journal = {Journal of Chemical Information and Modeling}, 
  year = {2007}, 
  abstract = {Highly predictive topological maximum cross correlation
                 (TMACC) descriptors for the derivation of
                 quantitative structure-activity relationships
                 (QSARs) are presented, based on the widely used
                 autocorrelation method. They require neither the
                 calculation of three-dimensional conformations nor
                 an alignment of structures. We have validated the
                 TMACC descriptors across eight literature data sets,
                 ranging in size from 66 to 361 molecules. In
                 combination with partial least-squares regression,
                 they perform competitively with a current
                 state-of-the-art 2D QSAR methodology, hologram QSAR
                 (HQSAR), yielding larger leave-one-out
                 cross-validated coefficient of determination values
                 (LOO q2) for five data sets. Like HQSAR, these
                 descriptors are also interpretable but do not
                 requiring hashing. The interpretation both enables
                 the automated extraction of SARs and can give a
                 description in qualitative agreement with more
                 time-consuming 3D and 4D QSAR methods. Open source
                 software for generating the TMACC descriptors is
                 freely available from our Web site.}, 
  doi = {10.1021/ci050400b}