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Bravi G, Green DV, Hann MM, et al. PLUMS: a program for the rapid optimization of focused libraries. J Chem Inf Comput Sci. 2000;40(6):1441-8doi: 10.1021/ci000389+.
Bravi, G., Green, D. V., Hann, M. M., & Leach, A. R. (2000). PLUMS: a program for the rapid optimization of focused libraries. Journal of chemical information and computer sciences, 40(6), 1441-8. https://doi.org/10.1021/ci000389+
Bravi, G, et al. "PLUMS: a program for the rapid optimization of focused libraries." Journal of chemical information and computer sciences vol. 40,6 (2000): 1441-8. doi: https://doi.org/10.1021/ci000389+
Bravi G, Green DV, Hann MM, Leach AR. PLUMS: a program for the rapid optimization of focused libraries. J Chem Inf Comput Sci. 2000 Nov-Dec;40(6):1441-8. doi: 10.1021/ci000389+. PMID: 11128103.
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J Chem Inf Comput Sci. 2000 Nov-Dec;40(6):1441-8. doi: 10.1021/ci000389+.

PLUMS: a program for the rapid optimization of focused libraries.

Journal of chemical information and computer sciences

G Bravi, D V Green, M M Hann, A R Leach

Affiliations

  1. Computational Chemistry and Informatics, Glaxo Wellcome R&D, Medicines Research Centre, Stevenage, Hertfordshire, UK. [email protected]

PMID: 11128103 DOI: 10.1021/ci000389+

Abstract

PLUMS is a new method to perform rational monomer selection for combinatorial chemistry libraries. The algorithm has been developed to optimize focused libraries with specific two-dimensional and/or three-dimensional properties. A preliminary step is the identification of those molecules in the initial virtual library which satisfy the imposed property constraints; we define these molecules as the virtual hits. From the virtual hits, PLUMS generates a starting library, which is the true combinatorial library that includes all the virtual hits. Monomers are then removed in an iterative fashion, thus reducing the size of the library. At each iteration, the worst monomer is removed. Each sublibrary is selected using a global scoring function, which balances effectiveness and efficiency. The iterative process continues until one is left with a library that consists entirely of virtual hits. The optimal library, which is the best compromise between effectiveness and efficiency, can then be selected according to the score. During the iterative process, equivalent solutions may well occur and are taken into account by the algorithm, according to a user-defined parameter. The number of monomers for each substitution site and the size of the library are parameters that can be either optimized or used to constrain the selection. The results obtained on two test libraries are presented. PLUMS was compared with genetic algorithms (GA) and monomer frequency analysis (MFA), which are widely used for monomer selection. For the two test libraries, PLUMS and GA gave equivalent results. MFA is the fastest method, but it can give misleading solutions. Possible advantages and disadvantages of the different methods are discussed.

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