title: Advances in multiparameter optimization methods for de novo drug design.
abstract: BACKGROUND
  A high-quality drug must achieve a balance of physicochemical and absorption, distribution, metabolism and elimination properties, safety and potency against its therapeutic target(s). Multiparameter optimization (MPO) methods guide the simultaneous optimization of multiple factors to quickly target compounds with the highest chance of downstream success. MPO can be combined with 'de novo design' methods to automatically generate and assess a large number of diverse structures and identify strategies to optimize a compound's overall balance of properties.
  
  
  METHODS
  The article provides a review of MPO methods and recent developments in the methods and opinions in the field. It also provides a description of advances in de novo design that improve the relevance of automatically generated compound structures and integrate MPO. Finally, the article provides discussion of a recent case study of the automatic design of ligands to polypharmacological profiles.
  
  
  CONCLUSIONS
  Recent developments have reduced the generation of chemically infeasible structures and improved the quality of compounds generated by de novo design methods. There are concerns about the ability of simple drug-like properties and ligand efficiency indices to effectively guide the detailed optimization of compounds. De novo design methods cannot identify a perfect compound for synthesis, but it can identify high-quality ideas for detailed consideration by an expert scientist.
authors.affiliation: Optibrium Ltd , 7221 Cambridge Research Park, Beach Drive, Cambridge, CB25 9TL , UK +44 1223 815902 ; +44 1223 815907 ; matt.segall@optibrium.com.
authors.forename: Matthew
authors.initials: M
authors.lastname: Segall
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D015195:Drug Design; D006801:Humans; D008962:Models, Theoretical
keywords: Pareto optimization; de novo design; desirability functions; desirability indices; drug-like properties; evolutionary algorithms; ligand efficiency indices; medicinal chemistry transformation rules; multiparameter optimization