Strategies of multi-objective optimization in drug discovery and development

Abstract

Introduction: Drug discovery and development is a typical multi-objective problem and its successes or failures depend on the simultaneous control of numerous, often conflicting, molecular and pharmacological properties. Multi-objective optimization strategies represent a new approach to capture the occurrence of varying optimal solutions based on trade-offs among the objectives taken into account. In view of this, multi-objective optimization aims to discover a set of satisfactory compromises that may in turn be used to find the global optimal solution by optimizing numerous dependent properties simultaneously.

Areas covered: The authors review the potential of multi-objective strategies in a number of fields including: drug library design; substructure mining; the derivation of quantitative structure–activity relationship models; ranking of docking poses. The authors also discuss the potential of multi-objective strategies in controlling competing properties for absorption, distribution, metabolism and elimination/toxicity optimization.

Expert opinion: It is very clear to those who work in drug discovery and development that the success of rational drug design is largely dependent on the control of a number of, often conflicting, objectives. Therefore, multi-objective optimization methods, which have recently been introduced to the field of molecular discovery, represent the ultimate frontier in chemoinformatics. The widespread use of these multi-objective techniques has provided new opportunities in medicinal chemistry as seen through its use in a number of applications for chemoinformatics both within academia and the pharmaceutical industry.

Keywords::

• chemoinformatics
• multi-criterion optimization
• multi-objective evolutionary algorithm
• multi-objective optimization

Notes

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