Ligand efficiency indices for effective drug discovery

TLDR

Drug discovery must optimize many variables, from physicochemical properties to toxicity and bioavailability, and structure‑based methods influence all stages, yet biological challenges limit their impact, prompting scrutiny of physicochemical properties and the emergence of ligand efficiency as a guide for fragment and lead selection. The paper introduces generalized ligand‑efficiency concepts, such as efficiency per dalton and per polar surface area, and discusses them in a broader context. The authors review preliminary results and trends using ligand efficiencies to guide drug discovery and outline how to integrate them into structure‑based design for greater effectiveness and numerical robustness.

Abstract

Successful drug discovery requires the optimization of a large number of variables ranging from strictly physicochemical parameters such as molecular weight to more complex parameters related to toxicity and bioavailability. Presently, structure-based methodologies influence many aspects of the drug discovery process from lead discovery to the final preclinical characterization. However, critical biological issues along the path to the market have diminished the impact and power of this methodology. The physicochemical properties of the novel chemical entities designed and guided by structural methods have become the subject of intense scrutiny from lead discovery to drug candidate. The idea of ligand efficiency (binding energy/non-hydrogen atoms) has recently emerged as a useful guide to optimize fragment and lead selection in the discovery process. More generalized concepts of ligand efficiency, related to efficiency per dalton and per unit of polar surface area, have also been introduced and will be discussed in the broader context. Preliminary results and trends obtained using ligand efficiencies as guides are reviewed and their future application to guide drug discovery will be discussed, as well as their integration into the structure-based drug design methods to make them more effective and numerically robust.