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

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Structure-Activity Relationship (SAR) Analysis

Background:

• Ligand efficiency indices (LEIs) have evolved significantly over the past two decades in drug discovery.
• Numerous LEIs exist, with varying degrees of community acceptance and application.
• A unified perspective focusing on commonalities among LEIs is needed.

Purpose of the Study:

• To review and unify different ligand efficiency index (LEI) formulations.
• To propose a vector-based LEI concept for a more robust assessment framework.
• To explore the integration of LEIs into drug discovery workflows.

Main Methods:

• Analysis of numerical and algebraic relationships among existing LEIs.
• Conceptual extension of LEIs from scalar to vector variables.
• Discussion of LEI application in Structure-Activity Databases (SAR-Databases).

Main Results:

• Proposes a vector variable for LEIs, comprising 'direction' and 'distance' components.
• Highlights the potential of a vector approach to unify diverse LEI formulations.
• Suggests a framework for critically assessing and routinely incorporating LEIs.

Conclusions:

• A vector-based LEI concept can unify different formulations and provide algebraic robustness.
• This approach may facilitate the routine use of LEIs in drug discovery protocols.
• Community-driven testing is crucial to validate LEIs as reliable optimization variables.