Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
This addresses the need for 3-D generative models in chemistry, particularly for complex molecules beyond typical drug-like datasets, though it appears incremental as it extends existing encoding-decoding approaches to 3-D crystal structures.
The authors tackled the problem of generating 3-D crystal structures by developing a method to encode and decode atomic positions from a dataset of nearly 50,000 stable crystal unit cells, enabling tasks like random sampling, interpolation, and alteration of molecules.
Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative models have led to rapid progress in automated drug discovery. Many of the current methods focus on either 1-D or 2-D representations of typically small, drug-like molecules. However, many molecules require 3-D descriptors and exceed the chemical complexity of commonly used dataset. We present a method to encode and decode the position of atoms in 3-D molecules from a dataset of nearly 50,000 stable crystal unit cells that vary from containing 1 to over 100 atoms. We construct a smooth and continuous 3-D density representation of each crystal based on the positions of different atoms. Two different neural networks were trained on a dataset of over 120,000 three-dimensional samples of single and repeating crystal structures, made by rotating the single unit cells. The first, an Encoder-Decoder pair, constructs a compressed latent space representation of each molecule and then decodes this description into an accurate reconstruction of the input. The second network segments the resulting output into atoms and assigns each atom an atomic number. By generating compressed, continuous latent spaces representations of molecules we are able to decode random samples, interpolate between two molecules, and alter known molecules.