Metric Validation and the Receptor-Relevant Subspace Concept. Pearlman, R. S. & Smith, K. M. J.~Chem.~Inf.~Comput.~Sci., 39:28--35, 1999.
abstract   bibtex   
Following brief comments regarding the advantages of cell-based diversity algorithms and the selection of low-dimensional chemistry-space metrics needed to implement such algorithms, the notion of metric validation is discussed. Activity-seeded, structure-based clustering is presented as an ideal approach for the validation of either high- or low-dimensional chemistry-space metrics when validation by computer-graphic visualization is not possible. Whereas typical methods for reducing the dimensionality of chemistry-space inevitably discard potentially important information, we present a simple yet novel algorithm for reducing dimensionality by identifying which axes (metrics) convey information related to affinity for a given receptor and which axes can be safely discarded as being irrelevant to the given receptor. This algorithm often reveals a three- or two-dimensional subspace of a (typically six-dimensional) BCUT chemistry-space and, thus, enables computer graphic visualization of the actual coordinates of active compounds and combinatorial libraries. Most significantly, we illustrate the importance of using receptor-relevant distances for identifying near neighbors of lead compounds, comparing libraries, and other diversity-related tasks.
@article{Pearlman:1999aa,
	Abstract = { Following brief comments regarding the advantages of cell-based diversity
	algorithms and the selection of low-dimensional chemistry-space metrics
	needed to implement such algorithms, the notion of metric validation
	is discussed. Activity-seeded, structure-based clustering is presented
	as an ideal approach for the validation of either high- or low-dimensional
	chemistry-space metrics when validation by computer-graphic visualization
	is not possible. Whereas typical methods for reducing the dimensionality
	of chemistry-space inevitably discard potentially important information,
	we present a simple yet novel algorithm for reducing dimensionality
	by identifying which axes (metrics) convey information related to
	affinity for a given receptor and which axes can be safely discarded
	as being irrelevant to the given receptor. This algorithm often reveals
	a three- or two-dimensional subspace of a (typically six-dimensional)
	BCUT chemistry-space and, thus, enables computer graphic visualization
	of the actual coordinates of active compounds and combinatorial libraries.
	Most significantly, we illustrate the importance of using receptor-relevant
	distances for identifying near neighbors of lead compounds, comparing
	libraries, and other diversity-related tasks. },
	Author = {Pearlman, R. S. and Smith, K. M.},
	Date-Added = {2007-12-11 17:01:03 -0500},
	Date-Modified = {2008-04-25 11:50:54 -0400},
	Journal = {J.~Chem.~Inf.~Comput.~Sci.},
	Keywords = {diversity; cell; binning; bin},
	Owner = {rajarshi},
	Pages = {28--35},
	Timestamp = {2007.04.11},
	Title = {Metric Validation and the Receptor-Relevant Subspace Concept},
	Volume = {39},
	Year = {1999}}

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