Automatic Chemical Design using Variational Autoencoders

We train a variational autoencoder to convert discrete representations of molecules to and from a multidimensional continuous representation. This continuous representation allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the design of drug-like molecules as well as organic light-emitting diodes.