Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design

In recent years, machine learning (ML) models have been
found to
quickly predict various molecular properties with accuracy comparable
to high-level quantum chemistry methods. One such example is the calculation
of electrostatic potential (ESP). Different ESP prediction ML models
were proposed to generate surface molecular charge distribution. Electrostatic
complementarity (EC) can apply ESP data to quantify the complementarity
between a ligand and its binding pocket, leading to the potential
to increase the efficiency of drug design. However, there is not much
research discussing EC score functions and their applicability domain.
We propose a new EC score function modified from the one originally
developed by Bauer and Mackey, and confirm its effectiveness against
the available Pearson’s R correlation coefficient.
Additionally, the applicability domain of the EC score and two indices
used to define the EC score application scope will be discussed.