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Stochastic Algorithms for Maximizing Molecular Diversity Dimitris K. Agrafiotis

Summary: Stochastic Algorithms for Maximizing Molecular Diversity
Dimitris K. Agrafiotis
3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Suite 104, Exton, Pennsylvania 19341
Received May 15, 1997X
A common problem in the emerging field of combinatorial drug design is the selection of an appropriate
subset of compounds for chemical synthesis and biological evaluation. In this paper, we introduce a new
family of selection algorithms that combine a stochastic search engine with a user-defined objective function
that encodes any desirable selection criterion. The method is applied to the problem of maximizing molecular
diversity, and the results are visualized using Sammon's nonlinear mapping algorithm. By separating the
search method from the performance metric, the method can be easily extended to perform complex multi-
objective selections in advanced decision-support systems.
In recent years, combinatorial chemistry and high through-
put screening have revolutionized the way in which new drug
candidates are being discovered. As it is practiced today,
combinatorial chemistry is used merely as a source of
compounds for mass screening. While this approach is very
powerful, it still does not address the key, rate-limiting step
in drug discovery which is the elaboration of sufficient SAR
around a lead compound and the refinement of its pharma-


Source: Agrafiotis, Dimitris K. - Molecular Design and Informatics Group, Johnson & Johnson Pharmaceutical Research and Development


Collections: Chemistry; Computer Technologies and Information Sciences