Benchmarking the Predictive Power of Ligand Efficiency Indices in QSAR

Compound physicochemical
properties favoring in vitro potency are not always
correlated to desirable pharmacokinetic profiles.
Therefore, using potency (i.e., IC50) as the main criterion
to prioritize candidate drugs at early stage drug discovery campaigns
has been questioned. Yet, the vast majority of the virtual screening
models reported in the medicinal chemistry literature predict the
biological activity of compounds by regressing in vitro potency on topological or physicochemical descriptors. Two studies
published in this journal showed that higher predictive power on external
molecules can be achieved by using ligand efficiency indices as the
dependent variable instead of a metric of potency (IC50) or binding affinity (Ki). The present study aims at filling the shortage of a thorough assessment
of the predictive power of ligand efficiency indices in QSAR. To this
aim, the predictive power of 11 ligand efficiency indices has been
benchmarked across four algorithms (Gradient Boosting Machines, Partial
Least Squares, Random Forest, and Support Vector Machines), two descriptor
types (Morgan fingerprints, and physicochemical descriptors), and
29 data sets collected from the literature and ChEMBL database. Ligand
efficiency metrics led to the highest predictive power on external
molecules irrespective of the descriptor type or algorithm used, with
an R2test difference of ∼0.3 units and
a this difference ∼0.4 units when modeling small data sets
and a normalized RMSE decrease of >0.1 units in some cases. Polarity
indices, such as SEI and NSEI, led to higher predictive power than
metrics based on molecular size, i.e., BEI, NBEI, and LE. LELP, which
comprises a polarity factor (cLogP) and a size parameter (LE) constantly
led to the most predictive models, suggesting that these two properties
convey a complementary predictive signal. Overall, this study suggests
that using ligand efficiency indices as the dependent variable might
be an efficient strategy to model compound activity.