Applications of genetic algorithms in molecular diversity.
The definition of molecular diversity and the development of measures for assessing the similarity or dissimilarity of molecules are central tasks for the design of novel biologically active compounds. Combinatorial chemistry allows the coupling of mathematical optimisation methods that do not require the a priori knowledge of structure-activity relationships with the synthesis of biologically active compounds. Genetic algorithms that computationally mimic Darwinian evolution have proven to be useful in solving multidimensional problems and are now being used successfully in various areas of combinatorial chemistry. Applications have been developed that help in the selection of diverse compound libraries and in the synthesis of biologically active molecules.
Weber
L
L
Hoffmann-La Roche AG, Basel, Switzerland. lutz.weber@roche.com
D000465:Algorithms; D000595:Amino Acid Sequence; D005075:Biological Evolution; D002625:Chemistry, Organic / Q000379:methods*; D016384:Consensus Sequence; D015195:Drug Design; D008957:Models, Genetic; D019151:Peptide Library; D010455:Peptides / Q000737:chemistry; D013329:Structure-Activity Relationship
D019151:Peptide Library; D010455:Peptides