Molecular shape as a (useful) bias in chemistry

TL;DR

This paper introduces a fast, automated method for classifying molecular shapes into detailed categories, revealing biases in chemical space and correlating shape with key properties to enhance molecular design.

Contribution

A novel, efficient approach to classify molecular shapes into detailed categories across large datasets, improving understanding and guiding high-throughput molecular screening.

Findings

Current research is biased towards planar geometries.

Molecular shape classification correlates with properties like band gap and dipole moment.

Method applied to over one billion molecules.

Abstract

One of the molecular properties most intuitive to the human perception is the geometrical shape. However, when exploring a large chemical space the determination of shape needs to be automated. We present a fast and simple approach to identify a molecule as linear, planar, cube, cuboid, disk, elliptical disk, spheroid and sphere which is more fine grained than existing approaches. The method is applied to more than one billion molecules ranging from small organic molecules to whole proteins. The results show that current chemistry research is biased towards planar geometries. Moreover, we demonstrate that our molecular shape classification correlates with sought-after properties like the band gap, dipole moment, and heat capacity. This allows to increase the efficiency of molecular design studies by driving high-throughput-screening efforts towards desired values of molecular properties.