Genetic algorithms in molecular recognition and design.
Genetic algorithms provide a novel tool for the investigation of combinatorial optimization problems. A genetic algorithm takes an initial set of possible starting solutions, and iteratively improves them by means of crossover and mutation operators that are related to those involved in Darwinian evolution. This approach is illustrated by reference to applications in molecular modelling, the docking of flexible ligands into protein active sites and de novo ligand design.
Willett
P
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Krebs Institute for Biomolecular Research, Department of Information Studies, University of Sheffield, UK.
D000465:Algorithms; D000998:Antiviral Agents / Q000737:chemistry; D001665:Binding Sites; D003198:Computer Simulation; D016208:Databases, Factual; D004214:Distamycins / Q000737:chemistry; D019143:Evolution, Molecular; D008957:Models, Genetic; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry* / Q000235:genetics* / Q000378:metabolism; D012984:Software; D013762:Tetrahydrofolate Dehydrogenase / Q000737:chemistry
D000998:Antiviral Agents; D004214:Distamycins; D011506:Proteins; C030000:stallimycin; D013762:Tetrahydrofolate Dehydrogenase