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Dataset augmentation is a common way to deal with small datasets; Chemistrydatasets are often small. Spherical convolutional neural networks (SphNNs) andIcosahedral neural networks (IcoNNs) are a type of geometric machine learningalgorithm that maintains rotational symmetry. Molecular structure hasrotational invariance and is inherently 3-D, and thus we need 3-D encodingmethods to input molecular structure into machine learning. In this paper Ipresent Icospherical Chemical Objects (ICOs) that enable the encoding of 3-Ddata in a rotationally invariant way which works with spherical or icosahedralneural networks and allows for dataset augmentation. I demonstrate the ICOfeaturisation method on the following tasks: predicting general molecularproperties, predicting solubility of drug like molecules and the proteinbinding problem and find that ICO and SphNNs perform well on all problems.

Icospherical Chemical Objects (ICOs) allow for chemical data augmentation and maintain rotational, translation and permutation invariance