Ligand efficiency indices for effective drug discovery: a unifying vector formulation

Introduction The area of ligand efficiency indices (LEIs) in drug discovery has developed significantly since the initial publications nearly 20 years ago. A large number of different LEIs have been defined and applied with certain degrees of success and acceptance in the community. An overall view emphasizing more the common elements than the differences is needed. Areas covered In this review, the author accentuates the numerical and algebraic relationships among the different LEIs and proposes the notion of 'ligand efficiency index' (LEI) as a vector variable comprising two interrelated components that provide 'direction' and 'distance' along the drug discovery process. The same concept had been suggested before relating to the graphical representation of the content of Structure-Activity Databases (SAR-Databases). Expert opinion The extension of the concept of ligand efficiency from a scalar to a vector will help to unify the different formulations by emphasizing the relationship among the different variables. It should also provide an algebraically robust framework to critically assess the value of LEIs, and to incorporate them routinely in various workflows and protocols. Only cautious and rigorous testing by the community could provide a definitive proof of their possible value as reliable optimization variables in drug discovery.

Automated summary

The field of ligand efficiency indices (LEIs) in drug discovery has evolved significantly over the past two decades, with various LEIs defined and applied to a certain degree of success and acceptance in the community. This review emphasizes the numerical and algebraic relationships among different LEIs and introduces the concept of 'ligand efficiency index' (LEI) as a vector variable with two interrelated components to provide direction and distance in the drug discovery process. The extension of ligand efficiency from a scalar to a vector is proposed to unify different formulations and emphasize the relationship among variables, offering a robust framework to assess the value of LEIs and incorporate them into workflows and protocols in drug discovery. Rigorous testing by the community is needed to determine the possible value of LEIs as reliable optimization variables.