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 quantitatively apply ESP data to scale the complementarity between a ligand and its binding pocket, leading to the potential to increase efficiency of drug design. However, there is not much research discussing EC score functions and its application boundary. We propose a new EC score function modified from the one originally developed by Bauer and Mackey, and confirmed its effectiveness against the available Pearson’s R correlation coefficient. Additionally, the application boundary of the EC score and two indices used to define the EC score application scope will be discussed.
Protein and ligand structures
All protein-ligand complexes used in the study.
Activity data and EC scores
ESP calculation protocol, bioactivity data and EC scores.