PMID: 11911712Mar 26, 2002Paper

Chemical library subset selection algorithms: a unified derivation using spatial statistics

Journal of Chemical Information and Computer Sciences
Fred A HamprechtWilfred F van Gunsteren

Abstract

If similar compounds have similar activity, rational subset selection becomes superior to random selection in screening for pharmacological lead discovery programs. Traditional approaches to this experimental design problem fall into two classes: (i) a linear or quadratic response function is assumed (ii) some space filling criterion is optimized. The assumptions underlying the first approach are clear but not always defendable; the second approach yields more intuitive designs but lacks a clear theoretical foundation. We model activity in a bioassay as realization of a stochastic process and use the best linear unbiased estimator to construct spatial sampling designs that optimize the integrated mean square prediction error, the maximum mean square prediction error, or the entropy. We argue that our approach constitutes a unifying framework encompassing most proposed techniques as limiting cases and sheds light on their underlying assumptions. In particular, vector quantization is obtained, in dimensions up to eight, in the limiting case of very smooth response surfaces for the integrated mean square error criterion. Closest packing is obtained for very rough surfaces under the integrated mean square error and entropy criteria...Continue Reading

References

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Apr 13, 2000·Journal of Chemical Information and Computer Sciences·A Golbraikh

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Citations

Jul 19, 2011·Journal of Chemical Information and Modeling·Vincent Le GuillouxLuc Morin-Allory

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