2019 Volume 20 Pages 56-64
Finding direct correlations between electronic structures of molecules and their properties, which we call “electronic-structure informatics”, is one of the challenging issues in chemoinformatics because the electronic degree of freedom is an essential factor determining the chemical characteristics. Herein we develop computational methods to automatically draw two types of orbital correlation diagrams. They are expected useful to perform machine learning including electronic degrees of freedom. In the present approach, we focus on electronic similarity called orbital similarity whose score is defined as spatial overlap between two molecular orbitals (MOs) enclosed with their iso-value surfaces. The similarity scores are also used to derive another orbital correlation diagram called “orbital interaction diagram”. This diagram is to relate MOs of a target molecule with those of its fragments. Through applications to benzene derivatives, these diagrams are shown to be reasonable, indicating potential usefulness of the present method in machine learning for quantitative predictions of molecular properties and chemical reactivities.