UNDER CONSTRUCTION

Molecular descriptors and Ligand efficiency metrics

MetricRecommended valuesAverage drug valuesDescription
Simple descriptors
MWt (Molecular weight)368Molecular weight is a measure of the sum of the atomic weight values of the atoms in a molecule.
For a small molecule therapeutic to fall within Lipinski's rule of five, a molecule must have a molecular mass of less than 500 Da.
The Ghose drug-likeness filter sets molecular weight for a small molecule as 180 to 480 Da, whereas Veber’s Rule ignores the molecular weight cut-off parameter.
Retrospective analyses indicate the molecular weight of drugs is increasing over time. If other physicochemical properties such as lipophilicity are controlled then the molecular weight may not need to be limited by these guidelines but can impact on ligand efficiency of the molecule due to increase in the heavy atom count.
HA (Number of heavy atoms)<3625.9The heavy atom count of a molecule is the total number of non-hydrogen atom ('heavy' atom) within the chemical structure.
The use of molecular-size measures such as heavy atom count in ligand efficiency metrics has some caveats. All non-hydrogen component atoms, such as carbon, nitrogen, oxygen, sulphur and halogen are treated equally even though their sizes and binding properties are different, and some atoms in a molecule may not participate in receptor binding interactions.
To achieve a Ligand Efficiency (LE) value of 0.3 for pIC50 of 8.0, a molecule would have a HAC of ~37.
clogP (calculated octanol-water partition coefficient)1-33Log P is one of the most important molecule properties, having a significant influence on many ADMET-related parameters and overall compound 'quality'. http://doi.org/10.1517/17460441.2012.714363. The average clogP in marketed drugs has not changed over time (Shultz 2018)
clogD7.4 (calculated octanol-water distribution coefficient at pH7.4)1-31.59Takes into account ionisable groups in molecules. Log D values in the range 1 to 3 are more likely to avoid issues associated with excessive lipophilicity (poor solubility, high metabolic clearance, hERG inhibition, toxicity, promiscuity, CYP450 inhibition) or low lipophilicity (poor absorption, renal clearance). http://doi.org/10.1517/17460441.2012.714363.
HBA (Number of H-bond acceptor atoms)≤ 104.7The total number of NH and OH bonds (from Lipinski's rules). https://www.sciencedirect.com/science/article/abs/pii/S0169409X00001290?via%3Dihub. For Leads (RO3) <=3 https://www.sciencedirect.com/science/article/abs/pii/S1359644603028319?via%3Dihub. The average number of HBA in marketed drugs is increasing over time (Shultz 2018)
HBD (Number of H-bond donor atoms)≤ 51.9The total number of N or O atoms (from Lipinski's rules). https://www.sciencedirect.com/science/article/abs/pii/S0169409X00001290?via%3Dihub . For Leads (RO3) <=3 https://www.sciencedirect.com/science/article/abs/pii/S1359644603028319?via%3Dihub. The average number of HBD in marketed drugs has not changed over time (Shultz 2018)
tPSA (topological polar surface area)≤ 140Å274.3The surface sum over all polar atoms in molecules, primarily oxygen and nitrogen, also including their attached hydrogen atoms. Can be used in combination with <=10 rotatable bonds (Veber's rule for orally active compounds in rat). https://pubs.acs.org/doi/10.1021/jm020017n
nRotB (Number of rotatable bonds)≤ 104.9Any single non-ring bond, attached to a non-terminal, non-hydrogen atom. Amide C-N bonds are not counted because of their high barrier to rotation. Can be used in combination with <=140 Å2 tPSA (Veber's rule for orally active compounds in rat). https://pubs.acs.org/doi/10.1021/jm020017n. The mean value for rotatable bonds per molecule in drugs is 6. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-S15-S10
nAr (Number of aromatic rings)< 4
Remove or replace carboaromatic rings
2The mean aromatic ring count of compounds in development tends to be lower in later clinical phases, and lower still in marketed drugs. A higher number of aromatic rings has a negative impact on solubility, whilst increasing protein binding, CYP450 inhibition, and hERG inhibition, which is not simply due to changes in size or lipophilicity. Reducing the number of carboaromatic rings, by replacing them with heteroaromatic rings or aliphatic rings, significantly increases ADMET developability. http://doi.org/10.1016/j.drudis.2010.11.014
Fsp3 (Fraction of carbon atoms that are sp3 hybridised)0.4Fsp3, the fraction of carbon atoms that are sp3 hybridised (also known as the Aliphatic Indicator), is a 2-D descriptor used as a surrogate for three-dimensionality. It is expressed as a value between zero and one. Compounds with higher values of Fsp3 appear to exhibit higher solubility, lower melting points, less promiscuity, less protein binding, and less CYP450 inhibition. Fsp3 is negatively correlated with nAr (r = -0.61).
Efficiency metrics
LE (Ligand Efficiency = 1.4(-logIC50)/Heavy Atom Count)≥ 0.30.44The Ligand Efficiency (LE) concept was derived from the observation that the maximum affinity achievable by ligands is −1.5 kcal per mole per non-hydrogen atom ('heavy' atom), ignoring simple cations and anions, and from studies examining functional group binding energy.
https://doi.org/10.1038/nrd4163
Ligand efficiency (LE) metrics are calculated in a simple way and can be applied at all stages of drug discovery to evaluate fragments, screening hits, leads and candidate drugs. The use of molecular-size measures such as heavy atom count (HAC) in LE metrics treats all component atoms equally even though their sizes and binding properties are different, and some atoms in a molecule may not participate in receptor binding interactions.
The historical analysis of the use of ligan efficiency metrics in drug discovery provides a strong case for optimising molecules using LE to identify compounds that meet the project objectives with as high as LE values possible for the target class. The absolute LE value of a drug candidate may therefore be of less importance.
https://doi.org/10.1021/acs.jmedchem.1c00416
A guide not a prerequisite - an LE of 0.3 for pIC50 of 8.0 would be for a molecule with a HAC of ~37 and a molecular weight of 518 for a mean HA weight of 14.
BEI (Binding efficiency index = [pKd × 1000] ÷ MWtIdealised reference value = 27. The closer to this value the better. 22.6BEI has strong correlation with LE. BEI reflects the value of each atom's contribution of a ligand to binding potency through inclusion of a molecular weight term to provide an easy and effective ranking of molecules. Can be used in conjunction with SEI. https://www.sciencedirect.com/science/article/pii/S1359644605033866?via%3Dihub
SILE (Size-independent LE = pKd ÷ HA^0.3)No value recommended by metric originator4SILE is similar to LE and BEI but is designed to overcome the negative correlation with heavy atom count seen for LE and BEI. SILE has strong correlation with FQ. https://pubs.acs.org/doi/10.1021/ci900094m
FQ (Fit quality = [pKd ÷ HA] ÷ [0.0715 + (7.5328 ÷ HA) + (25.7079 ÷ HA2) − (361.4722 ÷ HA3)]No value recommended by metric originator but values 1 and above have "exceptional efficiencies". "Compounds with the best ligand efficiency...have FQ scores falling aorund 1.0" 1.07FQ is similar to LE and BEI but is designed to overcome the negative correlation with heavy atom count seen for LE and BEI. FQ has a strong correlation with SILE. https://pubs.acs.org/doi/10.1021/jm701255b
LLE or LipE (Lipophilic Ligand Efficiency = pIC50 − cLogP)Preferred 5-7 - higher value if possible4.6A guide not a prerequisite - an alternative efficiency metric that uses logP as the normalising quantity rather than the number of heavy atoms. Lipophilicity may perhaps be more important to control for that molecular weight.
LELP (LE price paid in lipophilicity = ALogP ÷ LE)8.04
LLEAT (LLE adjusted for HA count = 0.111 + [(1.37× LLE) ÷ HA])0.39
SEI (Surface-binding efficiency index =[pKd × 100] ÷ PSA)Ref. value = 1817.7SEI is defined as the pKi, pKd, or pIC50 per PSA (where 100 Å2 is used as a normalizing factor for
PSA values) e.g. A compound with an affinity of 1 nM and a PSA of 50 Å2 will have a SEI of 18. https://www.sciencedirect.com/science/article/abs/pii/S1359644605033866?via%3Dihub
AEI (ADME efficiency index = ([pKd − |ALogP|] ÷ PSA) × 100> 7 good; < 4 bad9.4A modification of LLE that incorporates a polarity measure (PSA). High AEI minimises the risk of transporter interactions, and often results in lower daily doses. http://dx.doi.org/10.1016/j.drudis.2015.09.010
Composite descriptors
QED (Quantitative estimate of drug-likeness)0.5-1.0 better; 0.0-0.5 worse0.61A composite score related to the molecule properties of oral drugs, using MWt, clog P, HBA, HBD, tPSA, nRotB, nAr, & structural alerts. Compounds with higher values of QED have properties more similar to those of existing oral drugs. http://doi.org/10.1038/NCHEM.1243
PFI (property forecast index = clogD + nAr)< 5 good; > 7 bad3.6High PFI is associated with poor ADMET outcomes, such as low solubility, low permeability, high protein binding, high CYP450 inhibition, high clearance, hERG inhibition, and off-target promiscuity. The sum of clogD (or clogP) and nAr is often more predictive than the separate descriptors. http://doi.org/10.1016/j.drudis.2011.06.001
AB-MPS (|LogD7.4 − 3| + nAr + nRotB)≤ 148.9For molecules that fall outside Lipinski Rule of 5 space and particularly those with a molecular weight of > 500, this is a helpful guide. Compliance with AB-MPS gives "an increased probability of higher oral bioavailability" and it correaltes with rat bioavailability in bRo5 (beyond Rule-of-5 molecules). https://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.7b00717
Ro5 (also known as Lipinksi's Rules, or the Rule of 5)MWt ≤ 500, clogP <5, H-bond donors ≤ 5, H-bond acceptors ≤ 10Lipinski's rules are probably the most famous heuristic for oral drug design in the last 25 years. It is widely used and catalysed the development of many heuristics described here and elsewhere. Originally, Lipinski stated a molecule that complied with any three of the rules was more likely to be soluble (in water) and/or be permeable but it is widely interpreted to also apply to oral bioavailability. There are caveats to Lipinski's rules and they should be applied with careful thought in the context of the molecules being studied. doi:10.1016/S0169-409X(00)00129-0 and doi:10.1016/j.ddtec.2004.11.007
bRo5
FLI (Fraction lipophilicity Index = 2 x logP - logD)1-8
CNS MPO
BBB Score> 4