Parallel genetic algorithms for crystal structure prediction: Successes and failures in predicting bicalutamide polymorphs

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This paper describes the application of our distributed computing framework for crystal structure prediction, Modified Genetic Algorithms for Crystal and Cluster Prediction (MGAC), to predict the crystal structure of the two known polymorphs of bicalutamide. The paper describes our success in findin...

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Detalles Bibliográficos
Autores principales: Ferraro, M.B., Orendt, A.M., Facelli, J.C.
Formato: SER
Materias:
Crystal structure prediction
Drug polymorphism
Parallel genetic algorithms
Bicalutamide
Cluster prediction
Crystal conformation
Distributed Computing
Energy potential
Low energies
Lower energies
Modified genetic algorithms
Second optimization
Selection process
Spurious effects
Unit-cell volume
Cell membranes
Clustering algorithms
Computer science
Genetic algorithms
Intelligent computing
Parallel algorithms
Polymorphism
Crystal structure
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_03029743_v5754LNCS_n_p120_Ferraro
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
Descripción
Sumario:This paper describes the application of our distributed computing framework for crystal structure prediction, Modified Genetic Algorithms for Crystal and Cluster Prediction (MGAC), to predict the crystal structure of the two known polymorphs of bicalutamide. The paper describes our success in finding the lower energy polymorph and the difficulties encountered in finding the second one. The results show that genetic algorithms are very effective in finding low energy crystal conformations, but unfortunately many of them are not plausible due to spurious effects introduced by the energy potential function used in the selection process. We propose to solve this by using a multi objective optimization GA approach, adding the unit cell volume as a second optimization target. © 2009 Springer Berlin Heidelberg.

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