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Titolo:
Monte carlo methods for small molecule high-throughput experimentation
Autore:
Chen, LG; Deem, MW;
Indirizzi:
Univ Calif Los Angeles, Dept Chem Engn, Los Angeles, CA 90095 USA Univ Calif Los Angeles Los Angeles CA USA 90095 Los Angeles, CA 90095 USA
Titolo Testata:
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
fascicolo: 4, volume: 41, anno: 2001,
pagine: 950 - 957
SICI:
0095-2338(200107/08)41:4<950:MCMFSM>2.0.ZU;2-X
Fonte:
ISI
Lingua:
ENG
Soggetto:
COMBINATORIAL LIBRARY DESIGN; DRUG DISCOVERY; CHEMICAL LIBRARIES; GENETIC ALGORITHM; DIVERSITY; OPTIMIZATION; IDENTIFICATION; GENERATION; LANDSCAPES; EVOLUTION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
39
Recensione:
Indirizzi per estratti:
Indirizzo: Deem, MW Univ Calif Los Angeles, Dept Chem Engn, Los Angeles, CA 90095 USAUniv Calif Los Angeles Los Angeles CA USA 90095 es, CA 90095 USA
Citazione:
L.G. Chen e M.W. Deem, "Monte carlo methods for small molecule high-throughput experimentation", J CHEM INF, 41(4), 2001, pp. 950-957

Abstract

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%.

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Documento generato il 01/04/20 alle ore 11:34:15