Ligand efficiency measures assess molecular properties, such as size and lipophilicity, required for small molecules to bind to drug targets. These measures include ligand efficiency (LE = ΔG/HA) and lipophilic ligand efficiency (LLE = pIC50 or Ki-logP/D). They are used to optimize fragments, hits, and leads, and have shown that increasing affinity and reducing lipophilicity can be achieved simultaneously. Analysis of 480 target-assay pairs from the literature shows weak correlations between biological activity and physical properties, indicating that increasing activity does not always require enhancing physical properties. Recent marketed oral drugs often have high ligand efficiency and lipophilic ligand efficiency values. Only 1.5% of molecules per target have superior combined ligand efficiency and lipophilic ligand efficiency values. Optimizing ligand efficiencies based on molecular size and lipophilicity can reduce molecular inflation in medicinal chemistry and improve drug candidate developability. Ligand efficiency metrics, such as LE and LLE, are useful for evaluating drug-like chemical space and guiding lead discovery and optimization. They help in balancing potency and lipophilicity, and have been shown to improve ADMET and safety endpoints. Lipophilic ligand efficiency (LLE) is a key metric that combines in vitro potency and lipophilicity. It is particularly useful for assessing the enthalpic component of ligand binding. Optimizing LLE can lead to more potent and less lipophilic molecules, improving drug developability. Fragment-based drug discovery (FBDD) benefits from ligand efficiency metrics, which help in normalizing affinities of hits to identify the best starting points for optimization. Ligand efficiency metrics allow careful control of chemical properties during fragment-to-lead optimization. Examples show that adding atoms to fragments can significantly increase binding affinity without increasing lipophilicity. Group efficiency (GE) is also useful in evaluating the efficiency of atoms added to the original molecule. Ligand efficiency measures have been applied to various drug targets, including GPCRs, kinases, proteases, and nuclear hormone receptors. Analysis of recently marketed drugs shows that they often have high ligand efficiency and lipophilic ligand efficiency values. However, only 1.5% of molecules per target have superior combined ligand efficiency and lipophilic ligand efficiency values. This highlights the importance of optimizing ligand efficiencies in drug discovery. The application of ligand efficiency metrics in drug discovery is essential for selecting and optimizing fragments, hits, and leads. These metrics help in balancing potency and lipophilicity, and have been shown to improve ADMET and safety endpoints. Ligand efficiency metrics are also useful in evaluating the relative 'druggability' of human drug targets and target classes. Overall, ligand efficiency measures provide a valuable tool for drug discovery, helping to identify and optimize molecules with improved properties.Ligand efficiency measures assess molecular properties, such as size and lipophilicity, required for small molecules to bind to drug targets. These measures include ligand efficiency (LE = ΔG/HA) and lipophilic ligand efficiency (LLE = pIC50 or Ki-logP/D). They are used to optimize fragments, hits, and leads, and have shown that increasing affinity and reducing lipophilicity can be achieved simultaneously. Analysis of 480 target-assay pairs from the literature shows weak correlations between biological activity and physical properties, indicating that increasing activity does not always require enhancing physical properties. Recent marketed oral drugs often have high ligand efficiency and lipophilic ligand efficiency values. Only 1.5% of molecules per target have superior combined ligand efficiency and lipophilic ligand efficiency values. Optimizing ligand efficiencies based on molecular size and lipophilicity can reduce molecular inflation in medicinal chemistry and improve drug candidate developability. Ligand efficiency metrics, such as LE and LLE, are useful for evaluating drug-like chemical space and guiding lead discovery and optimization. They help in balancing potency and lipophilicity, and have been shown to improve ADMET and safety endpoints. Lipophilic ligand efficiency (LLE) is a key metric that combines in vitro potency and lipophilicity. It is particularly useful for assessing the enthalpic component of ligand binding. Optimizing LLE can lead to more potent and less lipophilic molecules, improving drug developability. Fragment-based drug discovery (FBDD) benefits from ligand efficiency metrics, which help in normalizing affinities of hits to identify the best starting points for optimization. Ligand efficiency metrics allow careful control of chemical properties during fragment-to-lead optimization. Examples show that adding atoms to fragments can significantly increase binding affinity without increasing lipophilicity. Group efficiency (GE) is also useful in evaluating the efficiency of atoms added to the original molecule. Ligand efficiency measures have been applied to various drug targets, including GPCRs, kinases, proteases, and nuclear hormone receptors. Analysis of recently marketed drugs shows that they often have high ligand efficiency and lipophilic ligand efficiency values. However, only 1.5% of molecules per target have superior combined ligand efficiency and lipophilic ligand efficiency values. This highlights the importance of optimizing ligand efficiencies in drug discovery. The application of ligand efficiency metrics in drug discovery is essential for selecting and optimizing fragments, hits, and leads. These metrics help in balancing potency and lipophilicity, and have been shown to improve ADMET and safety endpoints. Ligand efficiency metrics are also useful in evaluating the relative 'druggability' of human drug targets and target classes. Overall, ligand efficiency measures provide a valuable tool for drug discovery, helping to identify and optimize molecules with improved properties.