3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks

[danpol]

Convolutional neural networks (CNN) have been successfully used to handle three-dimensional data and are a natural match for data with spatial structure such as 3D molecular structures. However, a direct 3D representation of a molecule with atoms localized at voxels is too sparse, which leads to poor performance of the CNNs. In this work, we present a novel approach where atoms are extended to fill other nearby voxels with a transformation based on the wave transform. Experimenting on 4.5 million molecules from the Zinc database, we show that our proposed representation leads to better performance of CNN-based autoencoders than either the voxel-based representation or the previously used Gaussian blur of atoms and then successfully apply the new representation to classification tasks such as MACCS fingerprint prediction.

http://dx.doi.org/10.1021/acs.molpharmaceut.7b01134

[evancofer]

Edited to match the style of other posts, and used a URL that made the DOI a bit more obvious.