Optimizing the size and configuration of combinatorial libraries.
This paper addresses a major issue in library design, namely how to efficiently optimize the library size (number of products) and configuration (number of reagents at each position) simultaneously with other properties such as diversity, cost, and drug-like physicochemical property profiles. These objectives are often in competition, for example, minimizing the number of reactants while simultaneously maximizing diversity, and thus present difficulties for traditional optimization methods such as genetic algorithms and simulated annealing. Here, a multiobjective genetic algorithm (MOGA) is used to vary library size and configuration simultaneously with other library properties. The result is a family of solutions that explores the tradeoffs in the objectives. This is achieved without the need to assign relative weights to the objectives. The user is then able to make an informed choice on an appropriate compromise solution. The method has been applied to two different virtual libraries: a two-component aminothiazole library and a four-component benzodiazepine library.
Wright
Trudi
T
Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN, United Kingdom.
Gillet
Valerie J
VJ
Green
Darren V S
DV
Pickett
Stephen D
SD