Journal
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
Volume 14, Issue 5, Pages 449-466Publisher
KLUWER ACADEMIC PUBL
DOI: 10.1023/A:1008108423895
Keywords
automated structure generation; drug design; genetic algorithm; molecular modeling; QSAR; SMILES; variable mapping
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Rational drug design involves finding solutions to large combinatorial problems for which an exhaustive search is impractical. Genetic algorithms provide a novel tool for the investigation of such problems. These are a class of algorithms that mimic some of the major characteristics of Darwinian evolution. LEA has been designed in order to conceive novel small organic molecules which satisfy quantitative structure-activity relationship based rules (fitness). The fitness consists of a sum of constraints that are range properties. The algorithm takes an initial set of fragments and iteratively improves them by means of crossover and mutation operators that are related to those involved in Darwinian evolution. The basis of the algorithm, its implementation and parameterization, are described together with an application in de novo molecular design of new retinoids. The results may be promising for chemical synthesis and show that this tool may find extensive applications in de novo drug design projects.
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