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 R(2)test 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.