  Dataset augmentation is a common way to deal with small datasets; Chemistry
datasets are often small. Spherical convolutional neural networks (SphNNs) and
Icosahedral neural networks (IcoNNs) are a type of geometric machine learning
algorithm that maintains rotational symmetry. Molecular structure has
rotational invariance and is inherently 3-D, and thus we need 3-D encoding
methods to input molecular structure into machine learning. In this paper I
present Icospherical Chemical Objects (ICOs) that enable the encoding of 3-D
data in a rotationally invariant way which works with spherical or icosahedral
neural networks and allows for dataset augmentation. I demonstrate the ICO
featurisation method on the following tasks: predicting general molecular
properties, predicting solubility of drug like molecules and the protein
binding problem and find that ICO and SphNNs perform well on all problems.
