  By analogy with Monte Carlo algorithms, we propose new strategies for design
and redesign of small molecule libraries in high-throughput experimentation, or
combinatorial chemistry. Several Monte Carlo methods are examined, including
Metropolis, three types of biased schemes, and composite moves that include
swapping or parallel tempering. Among them, the biased Monte Carlo schemes
exhibit particularly high efficiency in locating optimal compounds. The Monte
Carlo strategies are compared to a genetic algorithm approach. Although the
best compounds identified by the genetic algorithm are comparable to those from
the better Monte Carlo schemes, the diversity of favorable compounds identified
is reduced by roughly 60%.
