Electronic-structure informatics study on classification of protein-ligand interaction strength

Abstract

For the drug discovery, the Fragment Molecular Orbital (FMO) method has attracted attention as a method for quantitatively evaluating the strength of a target protein-ligand interaction by electronic structure calculation. The FMO method is an excellent method that dramatically improves computational costs, but is currently not suitable for virtual screening of many compounds. For this reason, it is thought that it is necessary to use it in combination with a technique for narrowing down ligands that are candidates for new drugs in advance. In this study, we considered whether the strength of protein-ligand interaction could be reproduced only by electronic descriptors, representing the characteristics of the ligand, by machine learning using the FMO database. As a result of constructing a random forest regression model for p38 MAP kinase ligands, a good correlation was confirmed between the electronic descriptor and the interaction strength. Therefore, the obtained regression model is expected to allow for virtual screening of candidate compounds binding strongly to the target protein.