title: A genetic algorithm for the automated generation of small organic molecules: drug design using an evolutionary algorithm.
abstract: 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.
authors.affiliation: GALDERMA R&D, Sophia Antipolis, Valbonne, France. douguet1@caramail.com
authors.forename: D
authors.initials: D
authors.lastname: Douguet
authors.forename: E
authors.initials: E
authors.lastname: Thoreau
authors.forename: G
authors.initials: G
authors.lastname: Grassy
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D015195:Drug Design; D008957:Models, Genetic; D009154:Mutation; D021281:Quantitative Structure-Activity Relationship; D012176:Retinoids / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology; D012459:Salicylates / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology
chemical_list: D012176:Retinoids; D012459:Salicylates